Method and system for synchronously detecting times of infected multi-drug-resistant bacteria cases based on MapReduce and big data management

文档序号：831821 发布日期：2021-03-30 浏览：9次中文

阅读说明：本技术 基于MapReduce及大数据管理同期检出感染多耐药菌例次数的方法及系统 (Method and system for synchronously detecting times of infected multi-drug-resistant bacteria cases based on MapReduce and big data management ) 是由霍瑞林�建陈春平于 2020-11-13 设计创作，主要内容包括：本公开提供了一种基于MapReduce及大数据管理同期检出感染多耐药菌例次数的方法及系统,基于MapReduce框架,利用分布式系统下机器的并行计算能力,把超出一台服务器内存和存储限制的数百万、数千万住院人次计算医院感染多耐药菌的例次数划分成数千万、数亿的小任务,在多台机器上同时执行这些小任务,再通过汇总若干小任务的中间输出结果,生成最终结果。本发明能够对百万级、千万级、亿级住院人次的大数据进行按照省市区域、医院等级、医院床位、综合和专科、公立和民营等各种口径进行海量并行计算,对所有类别的多重耐药菌相关的感染进行管理,实现医院感染多耐药菌的例次数的精确统计,对医院感染进行整体评估,实现对医院感染多耐药菌的整体防控与管理。(The utility model provides a method and a system for synchronously detecting the times of infected multi-drug resistant bacteria cases based on MapReduce and big data management, which divides the times of millions and millions of inpatients which exceed the memory and storage limit of a server into tens of millions and hundreds of millions of small tasks based on the MapReduce framework and by utilizing the parallel computing capability of machines under a distributed system, simultaneously executes the small tasks on a plurality of machines, and generates a final result by summarizing the intermediate output results of the small tasks. The invention can carry out massive parallel calculation on the big data of million-level, million-level and hundred million-level inpatient times according to various calibers such as provincial and urban areas, hospital levels, hospital beds, comprehensive and special departments, public standings, civil camps and the like, manages the infection related to multiple drug-resistant bacteria of all categories, realizes the accurate statistics of the times of cases of the multiple drug-resistant bacteria infected by hospitals, carries out the integral evaluation on the hospital infection, and realizes the integral prevention, control and management on the multiple drug-resistant bacteria infected by hospitals.)

1. A method for synchronously detecting the times of infection multi-drug resistant bacteria cases based on MapReduce and big data management is characterized by comprising the following steps:

s1, obtaining hospitalization process information A, referral information B, bacteria culture information J, drug sensitivity test information K, selected statistical time and selected departments, and determining the authority departments of the user according to the identity information of the user;

s2, dividing the branch information B into branch information B (a) and Y with the crossed time and the statistical time and branch information B (a) and N with the non-crossed time and the statistical time;

s3, dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;

s4, dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;

s5, judging whether the branch information B (c) Y contains branch records, if yes, executing step S6, and if not, outputting the example frequency of synchronously detected multi-drug resistant bacteria causing hospital infection as 0;

s6, acquiring hospitalization process information A of the patient, and acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, wherein the hospitalization time and the discharge time are jointly used as parameters g.MC2;

s7, dividing the bacterial culture information J into bacterial culture information J (a) Y that is submitted during hospitalization of the patient and bacterial culture information J (a) N that is not submitted during hospitalization of the patient based on the parameter g.mc2;

s8, dividing the bacterial culture information J (a) Y into bacterial culture information J (b) Y which is transmitted at the statistical time and bacterial culture information J (b) N which is not in the statistical time range;

s9, dividing the bacteria culture information J (b) _ Y into bacteria culture information J (c) _ Y for delivery in the user authority department range and bacteria culture information J (c) _ N for delivery out of the user authority department range based on the authority department;

s10, dividing the bacteria culture information J (c) _ Y into bacteria culture information J (d) _ Y for delivery in the delivery department selected by the user and bacteria culture information J (c) _ N for delivery in the scope not in the delivery department based on the selected department;

s11, dividing the bacteria culture information J (d) Y into bacteria culture information J (e) Y with an infection type of HA and bacteria culture information J (e) N with an infection type of HA;

s12, acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

s13, dividing the drug sensitivity test information K into drug sensitivity test information K (a) Y corresponding to the bacteria culture information and drug sensitivity test information K (a) N of other bacteria based on the parameter g.MRO;

s14, based on the culture result, dividing the drug sensitivity test information K (a) _ Y into drug sensitivity test information K (b1) _ Y of which the culture result is the Escherichia coli and Klebsiella pneumoniae and drug sensitivity test information K (b1) _ N of which the culture result is not the Escherichia coli and the Klebsiella pneumoniae;

s15, dividing the drug susceptibility test information K (b1) _ Y into drug susceptibility test information K (c1) _ Y of carbapenem-resistant or tricot cephalosporin and drug susceptibility test information K (c1) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;

s16, based on the culture result, dividing the drug sensitivity test information K (a) _ Y into drug sensitivity test information K (b2) _ Y of which the culture result is enterococcus faecalis and enterococcus faecium and drug sensitivity test information K (b2) _ N of which the culture result is not enterococcus faecalis and enterococcus faecium;

s17, dividing the drug susceptibility test information K (b2) _ Y into drug susceptibility test information K (c2) _ Y of vancomycin and drug susceptibility test information K (c2) _ N of non-vancomycin based on the drug susceptibility medicament;

s18, dividing the drug sensitivity test information K (a) Y into drug sensitivity test information K (b3) Y with the culture result of acinetobacter baumannii or pseudomonas aeruginosa and drug sensitivity test information K (b3) N with the culture result of not acinetobacter baumannii or pseudomonas aeruginosa based on the culture result;

s19, dividing the drug susceptibility test information K (b3) _ Y into drug susceptibility test information K (c3) _ Y in the range of appointed carbapenem resistance and drug susceptibility test information K (c3) _ N in non-appointed content based on the drug susceptibility drugs;

s20, dividing the drug sensitivity test information K (a) Y into drug sensitivity test information K (b4) Y with a culture result of Staphylococcus aureus and drug sensitivity test information K (b4) N with a culture result of not Staphylococcus aureus based on the culture result;

s21, dividing the drug sensitivity test information K (b4) _ Y into drug sensitivity test information K (c4) _ Y for the drug sensitivity medicine to be methicillin and drug sensitivity test information K (c4) _ N for the drug sensitivity medicine to be other drug sensitivity medicine based on the drug sensitivity medicine;

s22, merging the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain drug sensitivity test information K (d), and dividing the drug sensitivity test information K (d) into drug sensitivity test information K (e) with drug sensitivity results of intermedium or drug resistance and drug sensitivity test information K (e) with drug sensitivity results of non-intermedium and drug resistance based on drug sensitivity results;

and S23, outputting the times of the detection of multiple drug resistance cases in the hospitalized patient based on the number recorded in the drug susceptibility test information K (e) Y.

2. The management method according to claim 1, wherein the hospitalization procedure information includes patient case number, hospital admission department, hospital admission time, hospital discharge department, and hospital discharge time; the information of the branch department comprises the patient case number, the department, the time of entering the department and the time of leaving the department; the bacterial culture information comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type; the drug sensitivity test information comprises a patient case number, a submission department, sampling time, a culture result, a sample number, drug sensitivity medicines and a drug sensitivity result.

3. The method according to claim 2, wherein the step S2 includes: the statistical time range is t1-t 2; if the time for entering and leaving the branch department is less than t1, or the time for entering and leaving the branch department is greater than t2, the information B (a) _ Y of the branch department which does not cross the statistical time is contained, and filtering is carried out.

4. The method according to claim 2, wherein the step S7 includes: and the parameter g, MNC is an array [ in _ time, out _ time ] formed by the admission time in _ time and the discharge time out _ time, and if the sampling time is more than or equal to the in _ time and less than or equal to the out _ time, the parameter g belongs to the bacterial culture information J (a) _ Y which is checked during the hospitalization period of the patient.

5. The method according to claim 2, wherein the step S8 includes: the statistical time range is t1-t 2; if the sampling time is not less than t1 and not more than t2, the bacteria culture information j (b) _ Y is included in the statistical time inspection.

6. A system for synchronously detecting the times of infection multi-drug resistant bacteria cases based on MapReduce and big data management is characterized by comprising:

the acquisition module is used for acquiring hospitalization process information A, transfer information B, bacteria culture information J, drug sensitivity test information K, selected statistical time and selected departments and determining the authority departments of the user according to the identity information of the user;

the first filtering module is used for dividing the branch information B into branch information B (a) Y with the time crossed with the statistical time and branch information B (a) N with the time not crossed with the statistical time;

the second filtering module is used for dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;

the third filtering module is used for dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;

the judging module is used for judging whether the branch information B (c) Y has branch records, if so, the first collecting module is called, and if not, the example times of synchronously detected hospital infection multi-drug-resistant bacteria are output to be 0;

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring hospitalization process information A of a patient, acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, and taking the hospitalization time and the discharge time as parameters g.MC2;

a fourth filtering module for dividing the bacteria culture information J into bacteria culture information J (a) Y that is submitted during hospitalization of the patient and bacteria culture information J (a) N that is not submitted during hospitalization of the patient based on the parameter g.mc2;

a fifth filtering module, configured to divide the bacteria culture information j (a) _ Y into bacteria culture information j (b) _ Y that is to be checked at a statistical time and bacteria culture information j (b) _ N that is not within a statistical time range;

a sixth filtering module, configured to divide the bacteria culture information j (b) _ Y into bacteria culture information j (c) _ Y for submission in the scope of the user's authorized department and bacteria culture information j (c) _ N for submission not in the scope, based on the authorized department;

a seventh filtering module, configured to divide the bacteria culture information j (c) _ Y into bacteria culture information j (d) _ Y for censorship in a censorship department selected by the user and bacteria culture information j (c) _ N for censorship in a scope not in the censorship department, based on the selected department;

an eighth filtering module, configured to divide the bacteria culture information j (d) _ Y into bacteria culture information j (e) _ Y whose infection type is HA and bacteria culture information j (e) _ N whose infection type is not HA;

the second acquisition module is used for acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

the ninth filtering module is used for dividing the drug susceptibility test information K into drug susceptibility test information K (a) Y corresponding to the bacteria culture information and drug susceptibility test information K (a) N of other bacteria based on the parameter g.MRO;

a tenth filtering module, configured to divide the susceptibility test information K (a) _ Y into susceptibility test information K (b1) _ Y whose culture result is ehrlichia coli and klebsiella pneumoniae and susceptibility test information K (b1) _ N whose culture result is not ehrlichia coli and klebsiella pneumoniae, based on the culture result;

an eleventh filtering module, configured to divide the drug susceptibility test information K (b1) _ Y into drug susceptibility test information K (c1) _ Y for drugs with drug susceptibility as carbapenem-resistant cephalosporin or tricetra-cephalosporin, and drug susceptibility test information K (c1) _ N for other drugs with drug susceptibility based on the drug susceptibility drugs;

a twelfth filtering module, configured to divide the drug sensitivity test information K (a) _ Y into drug sensitivity test information K (b2) _ Y whose culture result is enterococcus faecalis and enterococcus faecium and drug sensitivity test information K (b2) _ N whose culture result is not enterococcus faecalis and enterococcus faecium, based on the culture result;

a thirteenth filtering module, configured to divide the drug susceptibility test information K (b2) _ Y into drug susceptibility test information K (c2) _ Y for vancomycin and drug susceptibility test information K (c2) _ N for non-vancomycin based on the drug susceptibility drug;

a fourteenth filtering module, configured to divide the drug susceptibility test information K (a) _ Y into drug susceptibility test information K (b3) _ Y whose culture result is acinetobacter baumannii or pseudomonas aeruginosa and drug susceptibility test information K (b3) _ N whose culture result is not acinetobacter baumannii or pseudomonas aeruginosa, based on the culture result;

a fifteenth filtering module, configured to divide the drug susceptibility test information K (b3) _ Y into drug susceptibility test information K (c3) _ Y in a specified carbapenem-resistant class range and drug susceptibility test information K (c3) _ N in unspecified contents, based on the drug susceptibility drug;

a sixteenth filtering module for dividing the drug susceptibility test information K (a) _ Y into drug susceptibility test information K (b4) _ Y whose culture result is staphylococcus aureus and drug susceptibility test information K (b4) _ N whose culture result is not staphylococcus aureus, based on the culture result;

a seventeenth filtering module, configured to divide the drug susceptibility test information K (b4) _ Y into drug susceptibility test information K (c4) _ Y for which the drug susceptibility drug is methicillin and drug susceptibility test information K (c4) _ N for which the drug susceptibility drug is other drug susceptibility drug based on the drug susceptibility drug;

a merging and filtering module, configured to merge the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain drug sensitivity test information K (d), and based on a drug sensitivity result, divide the drug sensitivity test information K (d) into drug sensitivity test information K (e) whose drug sensitivity result is intermediate or drug-resistant and drug sensitivity test information K (e) whose drug sensitivity result is not intermediate or drug-resistant;

and the output module is used for outputting the times of detecting multiple drug resistant cases in the hospitalized patients based on the number recorded in the drug sensitivity test information K (e) Y.

7. The management system of claim 6, wherein the hospitalization procedure information includes patient case number, hospital admission department, hospital admission time, hospital discharge department, hospital discharge time; the information of the branch department comprises the patient case number, the department, the time of entering the department and the time of leaving the department; the bacterial culture information comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type; the drug sensitivity test information comprises a patient case number, a submission department, sampling time, a culture result, a sample number, drug sensitivity medicines and a drug sensitivity result.

8. The management system of claim 7, wherein the first filtering module comprises: the statistical time range is t1-t 2; if the time for entering and leaving the branch department is less than t1, or the time for entering and leaving the branch department is greater than t2, the information B (a) _ Y of the branch department which does not cross the statistical time is contained, and filtering is carried out.

9. The management system according to claim 7, wherein the fourth filtering module comprises: and the parameter g, MNC is an array [ in _ time, out _ time ] formed by the admission time in _ time and the discharge time out _ time, and if the sampling time is more than or equal to the in _ time and less than or equal to the out _ time, the parameter g belongs to the bacterial culture information J (a) _ Y which is checked during the hospitalization period of the patient.

10. The management system according to claim 7, wherein the fifth filtering module comprises: the statistical time range is t1-t 2; if the sampling time is not less than t1 and not more than t2, the bacteria culture information j (b) _ Y is included in the statistical time inspection.

Technical Field

The invention belongs to the technical field of management of multi-drug-resistant bacteria infection, and particularly relates to a method and a system for synchronously detecting the times of multi-drug-resistant bacteria infection cases based on MapReduce and big data management, which are particularly suitable for a scene that the data volume of a patient to be processed far exceeds the storage (magnetic disc) and the computing capacity (memory and CPU) of a server and the task can not be manually split and distributed.

Background

Multidrug-resistant bacteria refer to pathogenic bacteria with multidrug resistance. In particular to a microorganism which can resist three or more types of antibiotics at the same time, but not three types of antibiotics.

The multiple drug-resistant bacteria comprise nine kinds, namely methicillin-resistant staphylococcus aureus, vancomycin-resistant enterococcus faecalis, vancomycin-resistant enterococcus faecium, three-four generation cephalosporin escherichia coli, carbapenem-resistant escherichia coli, three-four generation cephalosporin klebsiella pneumoniae, carbapenem-resistant acinetobacter baumannii and carbapenem-resistant pseudomonas aeruginosa. Wherein the methicillin resistance refers to drug resistance of pathogens to any one of cefoxitin, oxacillin and methicillin; the resistance to the cephalosporins is that the pathogens are resistant to any one of cefotaxime, ceftriaxone, ceftazidime and cefepime; carbapenems are resistance to any of imipenem, meropenem, doripenem, and ertapenem by pathogens.

Because the drug-resistant strain is resistant to various clinically and generally used antibacterial drugs, the treatment after infection is difficult, and the fatality rate is high. The drug-resistant strains are wide in distribution, rapid in spread and easy to generate epidemic outbreaks, and bring difficulty to clinical treatment and control of nosocomial infection. In fact, with the unjustified use and abuse of antibacterial agents, the resistance rates of microorganisms are increasing. Therefore, the method has important significance for managing hospital infection multi-drug resistant bacteria.

The existing management of hospital infection multi-drug resistant bacteria mainly comprises the detection and discovery of the multi-drug resistant bacteria, and after the hospital infection multi-drug resistant bacteria are detected, the hospital infection multi-drug resistant bacteria are reported to a hospital infection management department in a telephone way and the like to perform prevention, control and treatment on corresponding cases. That is, the existing management of the hospital infection multi-drug resistant bacteria cannot manage the times of the cases of the multi-drug resistant bacteria, only can record a single case of the multi-drug resistant bacteria, cannot realize the overall prevention, control and management of the hospital infection multi-drug resistant bacteria, and cannot evaluate the hospital infection condition integrally.

Therefore, how to realize the number of times of synchronously detecting multiple drug-resistant bacteria cases causing nosocomial infection of inpatients in a specified time period becomes a problem to be solved urgently.

The times of detecting multiple drug-resistant bacteria causing hospital infection in the same period are relatively easy to calculate in one medical institution, the number of people discharged from one common medical institution such as the third-class A is about fifty thousand per year, and the number of people discharged from the national or provincial leaders is hundreds of thousands. The calculation of the key indexes is complex under the condition of large data of millions, billions and billions of inpatients of hundreds and thousands of medical institutions in provincial regions or nationwide ranges, 2749 third-level hospitals in the China in 2019, 9687 second-level hospitals in 2019 and 17487 thousands of inpatients in public hospitals, and the initial result of one-time statistical analysis needs to be calculated in the last year.

Therefore, how to develop standardization, standardization and homogenization hospital infection monitoring in hundreds of hospitals and thousands of hospitals in one area and realize the number of times of synchronously detecting multiple drug-resistant bacteria cases causing hospital infection of inpatients in a specified time period under the condition of big data of the inpatients becomes the most urgent problem to be solved for developing a regional information monitoring platform.

Disclosure of Invention

The invention aims to provide a method and a system for synchronously detecting the times of infected multi-drug resistant bacteria cases based on MapReduce and big data management aiming at the defects of the prior art. The invention can manage the infection related to all kinds of multi-drug-resistant bacteria under the condition of big data of inpatients, realize the accurate statistics of the times of cases of multi-drug-resistant bacteria infected in hospitals, carry out the integral evaluation on the infection in hospitals and realize the integral prevention, control and management of the multi-drug-resistant bacteria infected in hospitals.

In order to achieve the purpose, the invention adopts the following technical scheme:

a method for synchronously detecting the times of infection multi-drug resistant bacteria cases based on MapReduce and big data management comprises the following steps:

s11, dividing the bacteria culture information J (d) Y into bacteria culture information J (e) Y with an infection type of HA and bacteria culture information J (e) N with an infection type of HA;

s12, acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

and S23, outputting the times of the detection of multiple drug resistance cases in the hospitalized patient based on the number recorded in the drug susceptibility test information K (e) Y.

Further, the hospitalization process information comprises a patient case number, an admission department, admission time, a discharge department and discharge time; the information of the branch department comprises the patient case number, the department, the time of entering the department and the time of leaving the department; the bacterial culture information comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type; the drug sensitivity test information comprises a patient case number, a submission department, sampling time, a culture result, a sample number, drug sensitivity medicines and a drug sensitivity result.

Further, the statistical time ranges from t1 to t 2; if the time for entering and leaving the branch department is less than t1, or the time for entering and leaving the branch department is greater than t2, the information B (a) _ Y of the branch department which does not cross the statistical time is contained, and filtering is carried out.

And further, the parameter g.MNC is an array [ in _ time, out _ time ] formed by the admission time in _ time and the discharge time out _ time, and if the sampling time is more than or equal to the in _ time and less than or equal to the out _ time, the parameter g.MNC belongs to the bacterial culture information J (a) _ Y which is checked during the hospitalization period of the patient.

Further, the statistical time ranges from t1 to t 2; if the sampling time is not less than t1 and not more than t2, the bacteria culture information j (b) _ Y is included in the statistical time inspection.

The invention also provides a system for synchronously detecting the times of infected multi-drug resistant bacteria cases based on MapReduce and big data management, which comprises the following steps:

the second acquisition module is used for acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

Further, the first filtering module includes: the statistical time range is t1-t 2; if the time for entering and leaving the branch department is less than t1, or the time for entering and leaving the branch department is greater than t2, the information B (a) _ Y of the branch department which does not cross the statistical time is contained, and filtering is carried out.

Further, the fourth filtration module comprises: and the parameter g, MNC is an array [ in _ time, out _ time ] formed by the admission time in _ time and the discharge time out _ time, and if the sampling time is more than or equal to the in _ time and less than or equal to the out _ time, the parameter g belongs to the bacterial culture information J (a) _ Y which is checked during the hospitalization period of the patient.

Further, the fifth filtering module includes: the statistical time range is t1-t 2; if the sampling time is not less than t1 and not more than t2, the bacteria culture information j (b) _ Y is included in the statistical time inspection.

The invention discloses a specific implementation mode for synchronously detecting the times of cases of multi-drug resistant bacteria infected based on MapReduce and big data management in detail, which is characterized in that hospital process information, branch information, bacterial culture information, drug sensitive test information, selected statistical time and selected departments are utilized, the authority departments of users are determined according to the identity information of the users, the times of cases of different types of multi-drug resistant bacteria causing hospital infection of inpatients in a specified time period are determined respectively based on different multi-drug resistant bacteria and corresponding drug sensitive medicines, and the final times of cases are obtained by combining the times of the different types of multi-drug resistant bacteria. The invention can manage the infection related to all kinds of multi-drug-resistant bacteria, realize the accurate statistics of the times of cases of hospital infection multi-drug-resistant bacteria, carry out the integral evaluation on the hospital infection and realize the integral prevention, control and management on the hospital infection multi-drug-resistant bacteria.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be obtained from the drawings without inventive effort.

FIG. 1 is a schematic diagram illustrating the logic operation of the algorithm from step S1 to step S3 according to the present disclosure.

FIG. 2 is a schematic diagram illustrating the logic operation of the algorithm from step S4 to step S6 according to the present disclosure.

FIG. 3 is a schematic diagram illustrating the logic operation of the algorithm from step S7 to step S9 according to the present disclosure.

FIG. 4 is a schematic diagram illustrating the logic operation of the algorithm from step S10 to step S13 according to the present disclosure.

FIG. 5 is a schematic diagram illustrating the logic operation of the algorithm from step S14 to step S15 according to the present disclosure.

FIG. 6 is a schematic diagram illustrating the logic operation of the algorithm from step S16 to step S17 according to the present disclosure.

FIG. 7 is a schematic diagram illustrating the logic operation of the algorithm from step S18 to step S19 according to the present disclosure.

FIG. 8 is a schematic diagram illustrating the logic operation of the algorithm from step S20 to step S21 according to the present disclosure.

FIG. 9 is a schematic diagram illustrating the logic operation flow of the algorithm from step S22 to step S23 according to the present disclosure.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.

The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.

In the following examples, X (y) type specification:

x represents a data set of a certain type;

y represents a serial number used for distinguishing a front data set and a rear data set of the same type of data in different logic units;

x (y) represents a data set under different logical units for a certain type of data;

y represents a coincidence condition;

n represents nonconforming;

example one

As shown in fig. 1 to 9, the present embodiment provides a method for detecting the number of times of multiple drug-resistant bacteria infection in the same period based on MapReduce and big data management, including the following steps:

the number of cases of hospital-induced infection with multi-drug resistant bacteria detected in the hospitalized patients is the number of cases of hospital-induced infection with multi-drug resistant bacteria detected in the hospitalized patients in a given period.

The multi-drug resistant bacteria causing nosocomial infection should meet the following requirements: the patients are in hospital at the same time, namely the statistical time of the hospital admission and discharge periods of the patients is crossed; 2. the patient is checked to detect multi-drug resistant bacteria during hospitalization; 3. more than two kinds of multi-drug resistant bacteria are detected from the same specimen, and corresponding results need to be calculated respectively; 4. the patient has hospital infection, and the infection pathogenic bacteria are multiple drug-resistant bacteria; 5. the infection type of the pathogen is only counted as multiple drug-resistant bacteria of HA, and the infection type is not multiple drug-resistant bacteria of pollution, CA, field planting, HA repetition, CA repetition and field planting repetition; 6. the user selection condition is satisfied.

Therefore, the invention obtains the hospitalization process information A, the transfer information B, the bacteria culture information J, the drug sensitive test information K and the like to screen the cases of causing the nosocomial infection with the multi-drug resistant bacteria.

The hospitalization process information is used for integrally recording the hospitalization process of the patient, and specifically comprises the patient case number, the hospital admission department, the hospital admission time, the hospital discharge department and the hospital discharge time.

The branch information is used for recording the information of entering and leaving the department of each diagnosis and treatment department during the hospitalization period of the patient, and specifically comprises the patient case number, the department, the time of entering the department, the time of leaving the department and the like.

The bacteria culture information is used for recording the culture process and the culture result of the bacteria culture, and specifically comprises a patient case number, a submission department, a project name, sampling time, report time, a culture result, a sample number and a type.

The drug sensitivity test information is used for recording the output result of the drug sensitivity test, and specifically comprises a patient case number, a submission department, sampling time, a culture result, a sample number, drug sensitivity medicines and a drug sensitivity result.

The hospitalization process information A, the referral information B, the bacterial culture information J and the drug sensitivity test information K are information collected or input by hospital workers in the work process.

In addition, the invention selects statistical time and departments and manages the times of cases of hospital infection with multi-drug resistant bacteria detected in a specified time period and in the specified departments. The hospital data has corresponding privacy, so that the statistics and management of the hospital data in the invention require a user to acquire corresponding data authority. The data authority of the user is associated with the corresponding identity information, so that the invention determines the authority department of the user according to the identity information of the operation user, and manages the times of cases of hospital infection with multi-drug-resistant bacteria by detecting the data in the authority department.

S2, dividing the branch information B into branch information B (a) and Y with the crossed time and the statistical time and branch information B (a) and N with the non-crossed time and the statistical time;

the invention firstly screens the information B of the department transfer based on the statistical time, wherein the information B is the initial data set of the type of the department transfer of the corresponding patient. Y represents a qualified branch record, and N represents an unqualified branch record. The fact that the time is intersected with the statistical time means that the statistical time belongs to a time period when the patient is in the corresponding department, namely the statistical time is located between the time of entering the department and the time of leaving the department when the patient is in the corresponding department, and otherwise, the time is not intersected with the statistical time. The statistical time range is t1-t 2; if the time for entering and leaving the branch department is less than t1, or the time for entering and leaving the branch department is greater than t2, the information B (a) _ Y of the branch department which does not cross the statistical time is contained, and filtering is carried out.

S3, dividing the branch information B (a) _ Y into branch information B (b) _ Y of which the department belongs to the authority department and branch information B (b) _ N of which the department does not belong to the authority department based on the authority department;

because the authority of each user is different, the invention screens the branch information B (a) Y based on the authority department room, so that the data operated by the user is adaptive to the corresponding authority. And comparing the 'department' field in the branch information with the authority department, and judging whether the 'department' field belongs to the scope of the authority department. The department information b (b) _ Y is a department record in a department belonging to the authority range managed by the user, and the department information b (b) _ N is a department record in a department not belonging to the authority range managed by the user.

S4, dividing the branch information B (b) _ Y into branch information B (c) _ Y of which the department belongs to the selected department and branch information B (c) _ N of which the department does not belong to the selected department based on the selected department;

in the invention, the user can manage the inpatients aiming at a specific department, therefore, the invention screens the department information B (b) _ Y based on the selected department, so that the statistical and screening data is adaptive to the department selected by the user independently, the user can select the corresponding data according to the requirement, and the cases of detecting multiple drug resistance in the inpatients of a specific department are counted. And comparing the 'department' field in the branch information with the selected department, and judging whether the 'department' field belongs to the range of the selected department.

S5, judging whether the branch information B (c) and Y has branch records, if yes, executing step S6, and if not, outputting the example frequency of synchronously detected multi-drug resistant bacteria causing hospital infection as 0.

Specifically, the invention judges according to the branch records B (c) _ Y, if the patient has records after the three steps, the operation is continued downwards, if the patient has no records, the operation is ended, and the result is 0.

S6, acquiring hospitalization process information A of the patient, and acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, wherein the hospitalization time and the discharge time are jointly used as parameters g.MC2;

the hospitalization process information is used for integrally recording the hospitalization process of the patient, and specifically comprises the patient case number, the hospital admission department, the hospital admission time, the hospital discharge department and the hospital discharge time. The method comprises the steps of firstly obtaining the hospitalization process information A of a patient, and further obtaining the relevant information of the fields of the admission time and the discharge time, wherein the relevant information is jointly used as the parameter g.MC2.

S7, dividing the bacterial culture information J into bacterial culture information J (a) Y that is submitted during hospitalization of the patient and bacterial culture information J (a) N that is not submitted during hospitalization of the patient based on the parameter g.mc2;

normally, the sampling time of bacterial culture of a patient who detects specific multi-drug resistant bacteria is within the hospitalization time range of the patient, so the invention screens obviously wrong data according to the parameter g.MC2. Specifically, the invention filters out the bacterial culture information J (a) N which is not checked during the hospitalization period of the patient at the sampling time based on the comparison between the 'sampling time' field in the bacterial culture information and the parameter g.MC2 of the hospitalization and discharge time, and obtains the bacterial culture information J (a) Y with the sampling time within the hospitalization time range. MNC is an array [ in _ time, out _ time ] formed by the admission time in _ time and the discharge time out _ time, and if the sampling time is more than or equal to in _ time and less than or equal to out _ time, the parameter belongs to the bacteria culture information J (a) _ Y which is checked during the hospitalization period of the patient.

S8, dividing the bacterial culture information J (a) Y into bacterial culture information J (b) Y which is transmitted at the statistical time and bacterial culture information J (b) N which is not in the statistical time range;

the invention firstly screens the bacterial culture information J (a) Y based on the statistical time, and the submission time is crossed with the statistical time, namely the submission time is in the statistical time range. And comparing the 'sampling time' field in the bacterial culture information with the selected statistical time, and judging whether the 'sampling time' field belongs to the selected statistical time range. The statistical time range is t1-t 2; if the sampling time is not less than t1 and not more than t2, the bacteria culture information j (b) _ Y is included in the statistical time inspection.

S9, dividing the bacteria culture information J (b) _ Y into bacteria culture information J (c) _ Y for delivery in the user authority department range and bacteria culture information J (c) _ N for delivery out of the user authority department range based on the authority department;

because the authority of each user is different, the invention screens the bacteria culture information J (b) _ Y based on the authority department, so that the data operated by the user is adaptive to the corresponding authority. The 'submission department' field in the bacteria culture information is compared with the authority department, and whether the 'submission department' field belongs to the scope of the authority department or not is judged. Bacterium culture information j (c) _ Y is bacterium culture information belonging to a censorship within the authority range managed by the user, and bacterium culture information j (c) _ N is bacterium culture information not belonging to a censorship managed by the user.

S10, dividing the bacteria culture information J (c) _ Y into bacteria culture information J (d) _ Y for delivery in the delivery department selected by the user and bacteria culture information J (c) _ N for delivery in the scope not in the delivery department based on the selected department;

in the invention, the user can manage the multi-drug resistance cases aiming at specific departments, therefore, the invention screens the bacteria culture information J (c) Y based on the selected departments, so that the statistical and screening data is adaptive to the departments selected by the user independently, the user can select the corresponding data according to the requirement, and the cases of detecting the multi-drug resistance in the inpatients of the specific departments are counted. The 'inspection department' field in the bacteria culture information is compared with the selected department, and whether the 'inspection department' field belongs to the selected department range is judged.

S11, dividing the bacteria culture information J (d) Y into bacteria culture information J (e) Y with an infection type of HA and bacteria culture information J (e) N with an infection type of HA;

specifically, the present invention screens the bacterial culture information j (d) _ Y based on the "type" field in the bacterial culture information. When the "type" field is HA, then bacterial culture information j (e) Y belonging to the infection type of HA, and otherwise bacterial culture information j (e) N belonging to the infection type of HA. The pathogen whose infection type is HA type represents a nosocomial infection pathogen.

S12, acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

according to the bacterial culture information J (e) Y, the sample number parameter g.MRO corresponding to the bacterial culture is selected and obtained. The format of the test number is: sample number-culture result. The invention obtains each test number in the bacterial culture information J (e) Y, and all different test numbers jointly form a test number list parameter g.MRO. Specifically, the contents of the "sample number" and "culture result" fields in the bacteria culture information j (e) _ Y are extracted as the test numbers in common.

S13, dividing the drug sensitivity test information K into drug sensitivity test information K (a) Y corresponding to the bacteria culture information and drug sensitivity test information K (a) N of other bacteria based on the parameter g.MRO;

the drug sensitivity test information is related to the bacteria culture information, so that the drug sensitivity test information K is firstly screened based on the parameter g.MRO in the bacteria culture information, and the drug sensitivity test information K (a) Y corresponding to the bacteria culture information and the drug sensitivity test information K (a) N of other bacteria are obtained through filtering, so that the drug sensitivity test information corresponding to the parameter g.MRO is obtained. Specifically, the fields of 'sample number' and 'culture result' in the drug sensitivity test information are obtained, and the drug sensitivity test information with the inconsistent 'sample number' and 'culture result' with g.MRO is filtered.

S14, based on the culture result, dividing the drug sensitivity test information K (a) _ Y into drug sensitivity test information K (b1) _ Y of which the culture result is the Escherichia coli and Klebsiella pneumoniae and drug sensitivity test information K (b1) _ N of which the culture result is not the Escherichia coli and the Klebsiella pneumoniae;

specifically, the drug sensitivity test information K (a) Y is screened based on the field of 'culture result'. If the field of the culture result is the escherichia coli and the klebsiella pneumoniae, the culture result is the drug susceptibility test information K (b1) _ Y of the escherichia coli and the klebsiella pneumoniae, otherwise, the culture result is the drug susceptibility test information K (b1) _ N of the escherichia coli and the klebsiella pneumoniae.

S15, dividing the drug susceptibility test information K (b1) _ Y into drug susceptibility test information K (c1) _ Y of carbapenem-resistant or tricot cephalosporin and drug susceptibility test information K (c1) _ N of other drug susceptibility drugs based on the drug susceptibility drugs;

specifically, the drug susceptibility test information K (b1) _ Y is screened based on the drug susceptibility drug field in the drug susceptibility test information. When the 'drug susceptibility drug' field includes any one of carbapenem-resistant bacteria or cephalosporins, it belongs to drug susceptibility test information K (c1) _ Y, otherwise it belongs to drug susceptibility test information K (c1) _ N.

S16, based on the culture result, dividing the drug sensitivity test information K (a) _ Y into drug sensitivity test information K (b2) _ Y of which the culture result is enterococcus faecalis and enterococcus faecium and drug sensitivity test information K (b2) _ N of which the culture result is not enterococcus faecalis and enterococcus faecium;

specifically, the drug sensitivity test information K (a) Y is screened based on the field of 'culture result'. And if the 'culture result' field is enterococcus faecalis and enterococcus faecium, the drug sensitivity test information K (b2) _ Y of the enterococcus faecalis and the enterococcus faecium belongs to the culture result, otherwise, the drug sensitivity test information K (b2) _ N of the enterococcus faecalis and the enterococcus faecium belongs to the culture result.

S17, dividing the drug susceptibility test information K (b2) _ Y into drug susceptibility test information K (c2) _ Y of vancomycin and drug susceptibility test information K (c2) _ N of non-vancomycin based on the drug susceptibility medicament;

specifically, the drug susceptibility test information K (b2) _ Y is screened based on the drug susceptibility drug field in the drug susceptibility test information. When the 'drug susceptibility drug' field is vancomycin, the drug susceptibility test information belongs to drug susceptibility test information K (c2) _ Y, otherwise, the drug susceptibility test information belongs to drug susceptibility test information K (c2) _ N.

S18, dividing the drug sensitivity test information K (a) Y into drug sensitivity test information K (b3) Y with the culture result of acinetobacter baumannii or pseudomonas aeruginosa and drug sensitivity test information K (b3) N with the culture result of not acinetobacter baumannii or pseudomonas aeruginosa based on the culture result;

specifically, the drug sensitivity test information K (a) Y is screened based on the field of 'culture result'. And if the field of the culture result is Acinetobacter baumannii and Pseudomonas aeruginosa, the drug sensitivity test information K (b3) _ Y belongs to the culture result of the Acinetobacter baumannii and the Pseudomonas aeruginosa, otherwise, the drug sensitivity test information K (b3) _ N belongs to the culture result of the Acinetobacter baumannii and the Pseudomonas aeruginosa.

S19, dividing the drug susceptibility test information K (b3) _ Y into drug susceptibility test information K (c3) _ Y in the range of appointed carbapenem resistance and drug susceptibility test information K (c3) _ N in non-appointed content based on the drug susceptibility drugs;

specifically, the drug susceptibility test information K (b1) _ Y is screened based on the drug susceptibility drug field in the drug susceptibility test information. The "drug susceptibility drug" field belongs to the drug susceptibility test information K (c3) _ Y when it includes the designated carbapenem resistance, and belongs to the drug susceptibility test information K (c3) _ N otherwise.

S20, dividing the drug sensitivity test information K (a) Y into drug sensitivity test information K (b4) Y with a culture result of Staphylococcus aureus and drug sensitivity test information K (b4) N with a culture result of not Staphylococcus aureus based on the culture result;

specifically, the drug sensitivity test information K (a) Y is screened based on the field of 'culture result'. When the "culture result" field is Staphylococcus aureus, it belongs to the drug susceptibility test information K (b4) _ Y of which the culture result is Staphylococcus aureus, otherwise it belongs to the drug susceptibility test information K (b4) _ N of which the culture result is not Staphylococcus aureus.

S21, dividing the drug sensitivity test information K (b4) _ Y into drug sensitivity test information K (c4) _ Y for the drug sensitivity medicine to be methicillin and drug sensitivity test information K (c4) _ N for the drug sensitivity medicine to be other drug sensitivity medicine based on the drug sensitivity medicine;

specifically, the drug susceptibility test information K (b4) _ Y is screened based on the drug susceptibility drug field in the drug susceptibility test information. When the drug susceptibility drug field comprises methicillin, the drug susceptibility test information belongs to drug susceptibility test information K (c4) _ Y, otherwise, the drug susceptibility test information belongs to drug susceptibility test information K (c4) _ N.

S22, merging the K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain drug sensitivity test information K (d), and dividing the drug sensitivity test information K (d) into drug sensitivity test information K (e) with drug sensitivity results of intermedium or drug resistance and drug sensitivity test information K (e) with drug sensitivity results of non-intermedium and drug resistance based on drug sensitivity results;

the invention detects various multi-drug-resistant bacteria, respectively obtains drug sensitivity test information corresponding to different drug-resistant bacteria, combines K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain all the multi-drug-resistant bacteria drug sensitivity test information, and particularly obtains a drug sensitivity data record K (d) containing nine multi-drug-resistant bacteria drug sensitivity medicaments. Therefore, the invention combines the obtained drug susceptibility test results K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y to obtain drug susceptibility test information K (d). This step is to combine the results of the different types of the detected multiple drug-resistant bacteria.

The drug susceptibility test information K (d) is screened based on the drug susceptibility result field in the drug susceptibility test information. When the drug sensitivity result field comprises any one of the intermediate or drug resistance, the drug sensitivity test information belongs to the record of detecting multiple resistant drugs in the hospitalized patients, belongs to the drug sensitivity test information K (e) Y, and otherwise belongs to the drug sensitivity test information K (e) N.

And S23, outputting the times of the detection of multiple drug resistance cases in the hospitalized patient based on the number recorded in the drug susceptibility test information K (e) Y.

Specifically, the acquired susceptibility test information k (e) _ Y is recorded information on patients with detected multiple drug resistance among the hospitalized patients. If the drug susceptibility test information in the drug susceptibility test information K (e) Y is null, 0 is output, and if the drug susceptibility test information is not null, the number of the drug susceptibility test information K (e) Y is output, and the number of the cases is 0 as the number of the cases in which the multiple drug resistance is detected in the hospitalized patient.

The disclosure is further illustrated below with reference to specific examples:

type data participating in the operation:

hospitalization process information A, referral information B, microorganism culture information J and drug sensitivity test result K.

Hospitalization procedure information a:

patient's case number	Admission department	Time of admission	Discharge department	Time of discharge
					123456(1)	Neurology department	2019-01-01 00:00:12	Rehabilitation department	2019-01-12 03:00:12

Information B of the branch department:

patient's case number	Department's office	Time of entering the clinic	Time of delivery
				123456(1)	Neurology department	2019-01-01 00:00:12	2019-01-05 01:00:12
123456(1)	ICU	2019-01-05 01:00:12	2019-01-08 02:00:12
				123456(1)	Rehabilitation department	2019-01-08 02:00:12	2019-01-12 03:00:12

Microorganism information J:

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Staphylococcus aureus

Whole blood

968584

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Pseudomonas aeruginosa

Whole blood

968584

Pollution (b) by

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Acinetobacter baumannii

Whole blood

868485

Planting

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Enterococcus faecium

Whole blood

584995

Drug susceptibility test result K:

the statistical time is 2019-01-0100: 00:00 to 2019-01-1023: 59

The authority department: all departments

The user selects a department: ICU

The first step is as follows:

inputting: the records B of the department transfer and the statistical time [ 2019-01-0100: 00:00,2019-01-1023: 59:59] are output:

B(a)_Y：

patient's case number	Department's office	Time of entering the clinic	Time of delivery
				123456(1)	Neurology department	2019-01-01 00:00:12	2019-01-05 01:00:12
123456(1)	ICU	2019-01-05 01:00:12	2019-01-08 02:00:12
				123456(1)	Rehabilitation department	2019-01-08 02:00:12	2019-01-12 03:00:12

B(a)_N：

Patient's case number

Department's office

Time of entering the clinic

Time of delivery

The second step is as follows:

inputting: department record B (a) _ Y and authority department

And (3) outputting:

B(b)_Y：

patient's case number	Department's office	Time of entering the clinic	Time of delivery
				123456(1)	Neurology department	2019-01-01 00:00:12	2019-01-05 01:00:12
123456(1)	ICU	2019-01-05 01:00:12	2019-01-08 02:00:12
				123456(1)	Rehabilitation department	2019-01-08 02:00:12	2019-01-12 03:00:12

B(b)_N：

Patient's case number

Department's office

Time of entering the clinic

Time of delivery

The third step:

inputting: department records B (b) _ Y and department ICU selected by user

And (3) outputting:

B(c)_Y：

patient's case number	Department's office	Time of entering the clinic	Time of delivery
				123456(1)	ICU	2019-01-05 01:00:12	2019-01-08 02:00:12

B(c)_N：

Patient's case number	Department's office	Time of entering the clinic	Time of delivery
				123456(1)	Rehabilitation department	2019-01-08 02:00:12	2019-01-12 03:00:12
123456(1)	Neurology department	2019-01-01 00:00:12	2019-01-05 01:00:12

The fourth step:

inputting: the records B (c) _ Y of the branch department

123456(1)

ICU

2019-01-05 01:00:12

2019-01-08 02:00:12

And (3) outputting:

true (meaning continue downward operation)

The fifth step:

inputting: procedure A of hospitalization

And (3) outputting:

MC2 with a value of [ 2019-01-0100: 00:12,2019-01-1203: 00:12]

A sixth step:

inputting: bacterial culture record J and g.MC2, values of [ 2019-01-0100: 00:12,2019-01-1203: 00:12]

And (3) outputting:

J(a)_Y：

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Staphylococcus aureus

Whole blood

968584

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Pseudomonas aeruginosa

Whole blood

968584

Pollution (b) by

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Acinetobacter baumannii

Whole blood

868485

Planting

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Enterococcus faecium

Whole blood

584995

J(a)_N：

Patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

A seventh step of:

inputting: bacterial culture records j (a) _ Y and [ statistical time ], output for [ 2019-01-0510: 17:00,2019-01-0809: 15:00 ]:

J(b)_Y

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Staphylococcus aureus

Whole blood

968584

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Pseudomonas aeruginosa

Whole blood

968584

Pollution (b) by

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Acinetobacter baumannii

Whole blood

868485

Planting

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Enterococcus faecium

Whole blood

584995

J(b)_N

Patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

An eighth step:

inputting: bacteria culture j (b) _ Y and [ department of jurisdiction ], department selection ICU export:

and (3) outputting: j (c) _ Y:

J(c)_N：

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

A ninth step:

inputting: bacterial culture record J (c) _ Y and [ department selection ] of user selection, department selection IC

And (3) outputting:

J(c)_Y：

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Staphylococcus aureus

Whole blood

968584

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Pseudomonas aeruginosa

Whole blood

968584

Pollution (b) by

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Acinetobacter baumannii

Whole blood

868485

Planting

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Enterococcus faecium

Whole blood

584995

J(c)_N:

Patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

A tenth step:

inputting: bacterial culture record J (d) _ Y

And (3) outputting:

J(e)_Y：

patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Staphylococcus aureus

Whole blood

968584

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Enterococcus faecium

Whole blood

584995

J(e)_N：

Patient's case number

Inspection department

Name of item

Sampling time

Time of report

Results of the culture

Specimen (variants)

Sample number

Type (B)

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Pseudomonas aeruginosa

Whole blood

968584

Pollution (b) by

123456(1)

ICU

Blood culture

2019-01-05 10:17:00

2019-01-08 09:15:00

Acinetobacter baumannii

Whole blood

868485

Planting

An eleventh step:

inputting: bacterial culture record J (e) _ Y

And (3) outputting:

sample number-culture result parameter g.MRO, value of [ 968584-Staphylococcus aureus, 584995-enterococcus faecium ]

A twelfth step:

inputting: sample number-culture result parameter g.MRO and drug sensitivity test information K

And (3) outputting:

K(a)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Pseudomonas aeruginosa

968584

Imipenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Pseudomonas aeruginosa

968584

Cefazolin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Pseudomonas aeruginosa

968584

Cefuroxime

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Pseudomonas aeruginosa

968584

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Pseudomonas aeruginosa

968584

Teicoplanin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Acinetobacter baumannii

868485

Imipenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Acinetobacter baumannii

868485

Cefazolin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Acinetobacter baumannii

868485

Cefuroxime

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Acinetobacter baumannii

868485

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Acinetobacter baumannii

868485

Teicoplanin

Sensitivity of

A thirteenth step of:

inputting: drug sensitivity record k (a) _ Y output:

K(b1)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

K(b1)_N

A fourteenth step of:

inputting: drug sensitivity record K (b1) _ Y output:

K(c1)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

K(c1)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

A fifteenth step:

inputting: drug sensitivity record k (a) _ Y output:

K(b2)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Teicoplanin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Qingdamei (Saint George)Vegetable extract

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Cefuroxime

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Penicillin

Drug resistance

K(b2)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefoxitin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Penicillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Vancomycin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Tetracycline derivatives

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Erythromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Rifampicin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Azithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clindamycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Compound sulfamethoxazole

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clarithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Latamoxef

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Moxifloxacin hydrate

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Minocycline

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Ciprofloxacin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Meropenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Imipenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefazolin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefuroxime

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Teicoplanin

Sensitivity of

Sixteenth step:

inputting: drug sensitivity record K (b2) _ Y output:

K(c2)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

K(c2)_N

Seventeenth step:

inputting: drug sensitivity record k (a) _ Y output:

K(b3)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

K(b3)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefoxitin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Penicillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Vancomycin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Tetracycline derivatives

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Erythromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Rifampicin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Azithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clindamycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Compound sulfamethoxazole

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clarithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Latamoxef

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Moxifloxacin hydrate

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Minocycline

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Ciprofloxacin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Meropenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Imipenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefazolin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefuroxime

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Teicoplanin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Teicoplanin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Cefuroxime

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Penicillin

Drug resistance

An eighteenth step:

inputting: drug sensitive record K (b3) _ Y

And (3) outputting:

K(c3)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

K(c3)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

A nineteenth step:

inputting: drug sensitive record K (a) Y

And (3) outputting:

K(b4)_Y

K(b4)_N

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Teicoplanin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Cefuroxime

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Penicillin

Drug resistance

The twentieth step:

inputting: drug sensitive record K (b4) _ Y

And (3) outputting:

K(c4)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefoxitin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

K(c4)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Penicillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Tetracycline derivatives

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Erythromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Rifampicin

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Azithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clindamycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Compound sulfamethoxazole

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Clarithromycin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Latamoxef

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Moxifloxacin hydrate

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Minocycline

Sensitivity of

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Ciprofloxacin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Meropenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Imipenem

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefazolin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefuroxime

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Gentamicin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Teicoplanin

Sensitivity of

A twenty-first step:

inputting: the obtained drug sensitive record K (c1) _ Y, K (c2) _ Y, K (c3) _ Y, K (c4) _ Y

And (3) outputting:

K(d)

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefoxitin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

A twenty-second step:

inputting: drug sensitive record in all designations K (d)

And (3) outputting:

K(e)_Y

patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Cefoxitin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Staphylococcus aureus

968584

Oxacillin

Drug resistance

123456(1)

ICU

2019-01-05 10:17:00

Enterococcus faecium

584995

Vancomycin

Drug resistance

K(e)_N

Patient's case number

Inspection department

Sampling time

Results of the culture

Sample number

Medicine for treating drug allergy

The result of drug sensitivity

The twenty-third step:

inputting: drug sensitive record K (e) Y

And (3) outputting:

the count value is 3

Example two

The embodiment provides a system for synchronously detecting the times of infected multi-drug resistant bacteria cases based on MapReduce and big data management, which comprises:

and the judging module is used for judging whether the branch information B (c) and Y (Y) has a branch record, if so, the first acquisition module is called, and if not, the example times of synchronously detected hospital infection multi-drug-resistant bacteria are output and are 0.

The system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring hospitalization process information A of a patient, acquiring the hospitalization time and the discharge time of the patient based on the hospitalization process information, and taking the hospitalization time and the discharge time as parameters g.MC2;

the second acquisition module is used for acquiring a sample number parameter g.MRO based on the bacteria culture information J (e) Y;

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of each module is only one logic function division, and there may be another division manner in actual implementation. For example, multiple modules or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

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Method and system for synchronously detecting times of infected multi-drug-resistant bacteria cases based on MapReduce and big data management

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