阅读说明：本技术 药嘱风险提示方法、装置、电子设备及存储介质 (Medicine advises indicating risk method, apparatus, electronic equipment and storage medium ) 是由 胡腾 于 2019-08-30 设计创作，主要内容包括：本申请公开了药嘱风险提示方法、装置、电子设备及存储介质,涉及医疗大数据技术。具体实现方案为：通过先获取患者的临床信息,再将临床信息与预设药物知识图谱中的概念节点进行对应,以获得对应的概念节点集合,然后再根据概念节点集合以及概念节点集合中各概念节点之间的关系信息确定风险提示信息。本申请公开的药嘱风险提示方法,可以实现对药嘱中的药品使用进行风险提示。(This application discloses medicines to advise indicating risk method, apparatus, electronic equipment and storage medium, is related to medical big data technology.Specific implementation are as follows: by the clinical information for first obtaining patient, clinical information is carried out with the concept node in default knowledge of medication map again corresponding, to obtain corresponding concept node set, indicating risk information then is determined further according to the relation information between each concept node in concept node set and concept node set.Medicine disclosed in the present application advises indicating risk method, and the drug that may be implemented in advising medicine uses progress indicating risk.)

1. a kind of medicine advises indicating risk method characterized by comprising

The clinical information of patient is obtained, the clinical information includes that medicine advises information and diagnostic message；

Obtain the clinical information corresponding concept node set, knowledge of medication map packet in default knowledge of medication map Include the relation information between concept node information and each concept node；

Wind is determined according to the relation information in the concept node set and the concept node set between each concept node Dangerous prompt information, the indicating risk information are used to identify the medicine and advise drug application risk in information.

2. medicine according to claim 1 advises indicating risk method, which is characterized in that described to obtain the clinical information in medicine Corresponding concept node set in object knowledge mapping, comprising:

According to default classifying rules determine the clinical information corresponding to entity type set, in the entity type set Entity type is for characterizing patient attribute and/or drug attribute；

The concept node set is determined according to preset matching rule and the entity type set.

3. medicine according to claim 2 advises indicating risk method, which is characterized in that it is described according to preset matching rule and The entity type set determines the concept node set, comprising:

Instantiation concept node set is determined according to the preset matching rule and the entity type set；

Determine that concept extends node set according to the instantiation concept node set and the knowledge of medication map, it is described general Reading node set includes that the instantiated nodes set and the concept extend node set.

4. medicine according to claim 3 advises indicating risk method, which is characterized in that described according to the instantiation concept section Point set and the knowledge of medication map determine that concept extends node set, comprising:

Concept is obtained according to the instantiation concept node instantiated in concept node set and corresponding relation information to prolong Stretch node, extend node set to generate the concept, wherein the concept extend node and the instantiation concept node it Between the relation information be subordinate relation or inclusion relation.

5. medicine according to claim 4 advises indicating risk method, which is characterized in that described according to the concept node set And the relation information in the concept node set between each concept node determines indicating risk information, comprising:

It is determined according to the relation information in the concept node set and the concept node set between each concept node real Example knowledge subgraph；

Obtain the risk relations information in the Case knowledge subgraph, wherein the risk relations information is disabling relationship or careful With relationship；

The indicating risk information is determined according to the risk relations information.

6. medicine according to claim 5 advises indicating risk method, which is characterized in that believed described according to the risk relations Breath determines after the indicating risk information, further includes:

According to the risk relations information, the corresponding concept node of the risk relations information and default words art mapping table output The indicating risk information.

7. medicine described in any one of -6 advises indicating risk method according to claim 1, which is characterized in that the clinical information It further include at least one of patient physiological information, symptom information and patient of hypersensitivity's history information.

8. medicine described in any one of -6 advises indicating risk method according to claim 1, which is characterized in that in the acquisition institute Clinical information is stated in default knowledge of medication map before corresponding concept node set, further includes:

The default knowledge of medication map is generated according to medical data source.

9. medicine according to claim 8 advises indicating risk method, which is characterized in that described to generate institute according to medical data source State default knowledge of medication map, comprising:

It is obtained between concept keyword and each concept keyword in the medical data source according to default knowledge format Relation keyword；

The default knowledge of medication map is generated according to the concept keyword and the relation keyword, wherein described general Reading nodal information includes the concept keyword, and the relation information includes the relation keyword.

10. medicine according to claim 9 advises indicating risk method, which is characterized in that the basis is preset knowledge format and obtained Take the relation keyword between the concept keyword and each concept keyword in the medical data source, comprising:

The concept keyword is obtained from primary sources library according to the default knowledge format；

It is crucial that the relationship is obtained from the primary sources library and secondary sources library according to the default knowledge format Word, wherein default first confidence level in the primary sources library is greater than default second confidence level in the secondary sources library.

11. medicine according to claim 10 advises indicating risk method, which is characterized in that described crucial according to the concept Word and the relation keyword generate after the default knowledge of medication map, further includes:

Determine the confidence level of knowledge information in the default knowledge of medication map, wherein the knowledge information is the concept section Point information or the relation information；

First knowledge information is incorporated to the default knowledge of medication map, wherein first knowledge information is knowledge confidence More than or equal to the knowledge information of default knowledge confidence.

12. medicine according to claim 11 advises indicating risk method, which is characterized in that in the determination default drug In knowledge mapping after the confidence level of knowledge information, further includes:

Evaluation information is obtained, the evaluation information is used to evaluate the reliability of the second knowledge information, and second knowledge information is Knowledge confidence is less than the knowledge information of the default knowledge confidence；

Determined whether for second knowledge information to be incorporated to the default knowledge of medication map according to the evaluation information.

13. medicine according to claim 12 advises indicating risk method, which is characterized in that the determination default drug is known Know the knowledge confidence of knowledge information in map, comprising:

According to the default confidence level of the database in knowledge information institute source and the database in knowledge information institute source Quantity determine the knowledge confidence.

14. medicine according to claim 13 advises indicating risk method, which is characterized in that further include:

Knowledge information set to be verified is obtained, the knowledge information to be verified of the knowledge information set to be verified is known for described second Know information；

It is determined according to the knowledge information to be verified in the knowledge information set to be verified and gathers whole accuracy rate；

The knowledge information collection to be verified that the whole accuracy rate of the set is greater than or equal to default accuracy rate is merged into described default Knowledge of medication map.

15. medicine according to claim 14 advises indicating risk method, which is characterized in that be verified known described according to described Knowledge information to be verified in knowledge information aggregate determines after the whole accuracy rate of set, further includes:

The knowledge information set to be verified that the whole accuracy rate of the set is less than the default accuracy rate is split as multiple to school Test knowledge information subclass；

Subclass entirety accuracy rate is determined according to the knowledge information to be verified in the knowledge information subclass to be verified；

The knowledge information subset to be verified that the subclass entirety accuracy rate is greater than or equal to default accuracy rate is merged into described Default knowledge of medication map.

16. medicine according to claim 13 advises indicating risk method, which is characterized in that described according to the concept node After relation information in set and the concept node set between each concept node determines indicating risk information, also wrap It includes:

Feedback command is obtained, the feedback command is to receive instruction or refusal instruction, and the receiving instruction is described for receiving Indicating risk information, the refusal instruction is for refusing the indicating risk information.

17. medicine according to claim 16 advises indicating risk method, which is characterized in that the acquisition feedback command it Afterwards, further includes:

When the feedback command is to receive instruction, the knowledge confidence of knowledge information is increased by the first numerical value, when the feedback When instruction instructs for refusal, the knowledge confidence of knowledge information is reduced into second value；

If the knowledge confidence of the knowledge information is less than default knowledge confidence, the invalid knowledge information, if described When the knowledge confidence of knowledge information is greater than or equal to the default knowledge confidence, then the knowledge information is activated.

18. a kind of medicine advises indicating risk device characterized by comprising

Data obtaining module, for obtaining the clinical information of patient, the clinical information includes that medicine advises information and diagnostic message；

Information link block, for obtaining the clinical information corresponding concept node set in default knowledge of medication map, The knowledge of medication map includes the relation information between concept node information and each concept node；

Indicating risk module, for according in the concept node set and the concept node set between each concept node Relation information determine indicating risk information, the indicating risk information is used to identify the medicine and the drug in information is advised to use wind Danger.

19. a kind of electronic equipment characterized by comprising

At least one processor；And

The memory being connect at least one described processor communication；Wherein,

The memory is stored with the instruction that can be executed by least one described processor, and described instruction is by described at least one It manages device to execute, so that at least one described processor is able to carry out method described in any one of claim 1-17.

20. a kind of non-transitory computer-readable storage medium for being stored with computer instruction, which is characterized in that the computer refers to It enables for making the computer perform claim require method described in any one of 1-17.

Technical field

This application involves technical field of data processing more particularly to a kind of medical big data technologies.

Background technique

With the fast development of drug science and technology, knowledge of medication explosion proposes stern challenge to the work of doctor.

Detailed description of the invention

Attached drawing does not constitute the restriction to the application for more fully understanding this programme.Wherein:

Fig. 1 is the application scenarios schematic diagram that medicine provided by the embodiments of the present application advises indicating risk method；

Fig. 2 is knowledge of medication profile information provided by the embodiments of the present application source schematic diagram of a scenario；

Fig. 3 is the flow diagram that medicine shown in the application first embodiment advises indicating risk method；

Fig. 4 is clinical information data structure schematic diagram in embodiment illustrated in fig. 3；

Fig. 5 is the flow diagram of the concept node set acquisition modes shown in the application second embodiment；

Fig. 6 is the flow diagram of the indicating risk information method of determination shown in the application 3rd embodiment；

Fig. 7 is the flow diagram of the knowledge of medication map generating mode shown in the application fourth embodiment；

Fig. 8 is the flow diagram that the application the 5th implements the keyword acquisition modes exemplified；

Fig. 9 is the data structure schematic diagram of the Case knowledge subgraph shown in the application sixth embodiment；

Figure 10 is the flow diagram that the medicine that the application the 7th implements to exemplify advises indicating risk method；

Figure 11 is the flow diagram that the medicine that the application the 8th implements to exemplify advises indicating risk device；

Figure 12 is the flow diagram that the medicine that the application the 9th implements to exemplify advises indicating risk device；

Figure 13 is the flow diagram that the medicine that the application the tenth implements to exemplify advises indicating risk device；

Figure 14 is the flow diagram that the medicine that the application the 11st implements to exemplify advises indicating risk device；

Figure 15 is for realizing that the medicine of the embodiment of the present application advises the block diagram of the electronic equipment of indicating risk method.

Specific embodiment

It explains below in conjunction with exemplary embodiment of the attached drawing to the application, including the various of the embodiment of the present application Details should think them only exemplary to help understanding.Therefore, those of ordinary skill in the art should recognize It arrives, it can be with various changes and modifications are made to the embodiments described herein, without departing from the scope and spirit of the present application.Together Sample, for clarity and conciseness, descriptions of well-known functions and structures are omitted from the following description.

In the prior art, it in order to guarantee the drug safety to patient, is outputed after medicine advises in doctor, usually also needs to lead to Pharmacists is crossed to audit, to judge whether there is drug risk, such as: drug allergy, drug conflict and dosage misuse etc.. But since types of drugs quantity is various, and every kind of drug will include the medication points for attention of multiple dimensions again, therefore, according to Risk exclusion, inefficiency are carried out by artificially carrying out advising medicine, and reliability is lower.

Or pharmacopeia, pharmacology books, package insert, document and experience are referred to by pharmacists, for doctor Rule is reminded in the medication of every kind of drug hand-coding different dimensions inside institute, then be triggered to relevant risk implied condition it Afterwards, rational use of medicines prompting is carried out.But the mode of manual compiling, workload is huge, needs for all of online hospital, every family All dimension editors of drug remind rule, and National Drug Administration ratify self-produced or import drug up to hundreds of thousands of kinds it More, every kind of drug includes more than ten dimensions such as crowd, illness, interaction, incompatibility, solvent, usage and dosage, cross allergy again The knowledge of degree.In addition, different editorial staffs are when expressing same medication rule in the presence of words art specification, the difference of level of abstraction, volume Volume rule generated is not easy to trigger in actual use, generalization is poor.

Further, it is also possible to carry out drug risk prompt by black-box model method, wherein can be divided into has guidance and aphalangia Lead mode.There is guidance, can be using in hospital the clinical electronic health record generated and order data as training data, In, it is risk positive sample that pharmacists, which manually intercepts, modifies prescription, and executing through prescription is risk negative sample, using there is finger The machine learning method (such as: Bayes, decision tree, neural network) led trains drug risk prediction model.And without guidance Mode, then be mainly using between different drugs comprising passing through at fractional dimension, patient characteristic dimension (physiology, case history feature) Similarity calculation mode (such as: topic model, collaborative filtering) realizes that drug risk prompts in a manner of similar recommender system. But black-box model scheme, the biggest problems are that can not be explanatory, clinical application cannot provide indicating risk reason, be difficult Realize clinical aid decision.In addition, training data is more sparse, in actual training process, it is difficult to obtain in high volume high-quality Drug risk positive data is measured, negative data and positive data ratio difference are huge, simultaneously as pharmacists is difficult to all places Fang Jinhang is manually commented on one by one, therefore, results in negative historical data that there may be a large amount of positive data.Also, black box The prompting dimension of model realization is also very limited.

For above-mentioned each problem, the embodiment of the present application provides a kind of medicine and advises indicating risk method, by first obtaining Take the clinical information of patient, then clinical information carried out with the concept node in default knowledge of medication map it is corresponding, to obtain pair The concept node set answered, then further according to the relationship between each concept node in concept node set and concept node set Information determines indicating risk information, thus to medicine advise in drug using carry out indicating risk.Also, utilize knowledge of medication map The method for carrying out indicating risk, can preferably match with the progress of patient clinical information, optimize indicating risk toggle rate, and By way of generating indicating risk information according to relation information, the interpretation of indicating risk is enhanced.Below by several Specific implementation advises indicating risk method to be described in detail the medicine.

Fig. 1 is the application scenarios schematic diagram that medicine provided by the embodiments of the present application advises indicating risk method.As shown in Figure 1, this The medicine for implementing to provide advises indicating risk method to can be applied to medical scene, diagnoses in doctor to patient, and pass through medical treatment Terminal 200 is that patient outputs after medicine advises, and server 100 obtains the clinical information of patient, wherein clinical information includes that medicine advises letter Breath and diagnostic message.

And knowledge of medication map is preset in server 100, by will be in clinical information and default knowledge of medication map Concept node it is corresponding, so that it is determined that concept node set corresponding with clinical information, be worth explanation, knowledge of medication figure It include the relation information between concept node information and each concept node in spectrum.

Fig. 2 is knowledge of medication profile information provided by the embodiments of the present application source schematic diagram of a scenario.As shown in Fig. 2, drug The information source of knowledge mapping can be multiple medical data sources, wherein may include structured data source 300, semi-structured Data source 400 and Un-structured data source 500.It is worth explanation, for structured data source 300, such as can be with are as follows: medicine prison The Drug catalogue of office's approval listing, International Classification of Diseases (the International Classification of for defending strong committee's publication Diseases, abbreviation ICD) terminology standard etc.；And for semi-structured data source 400, such as can be with are as follows: pharmacopeia catalogue, data Catalogue, standard literature title and keyword and package insert part field etc.；And it can for Un-structured data source 500 Think the data such as clinical guidelines or path.

It is obtained in addition, can be to extract from multiple structuring subdatas source 310 for structured data source 300, and it is right It can be to extract from multiple semi-structured subdata sources 410 in semi-structured data source 400 and obtain, and for Un-structured number It can be to extract from multiple Un-structured subdatas source 510 according to source 500 and obtain.And in the present embodiment, not to medical number It is defined according to the concrete form and quantity in source, the classification of above-mentioned data source is intended merely to illustrate.

Fig. 3 is the flow diagram that medicine shown in the application first embodiment advises indicating risk method.As shown in figure 3, this The medicine that embodiment provides advises indicating risk method, comprising:

Step 101, the clinical information for obtaining patient.

It is worth explanation, clinical information may include that medicine advises information and diagnostic message.It can be and patient is outputed in doctor After medicine is advised, advise information and diagnostic message collectively as clinical information medicine.And in order to which the drug risk to patient carries out more The judgement of dimension, the clinical information of patient may include more information types.

Wherein, Fig. 4 is clinical information data structure schematic diagram in embodiment illustrated in fig. 3.As shown in figure 4, the clinic of patient Information, may include: the physiologic information (such as: age, gender etc.) of patient, symptom information (such as: symptom 1, symptom 2 etc.), Allergies information (such as: drug 3, drug 4 etc.), medicine are advised information (such as: drug 1, drug 2 etc.), and diagnostic message (such as: disease Disease 1, disease 2 etc.).In addition, being directed to every kind of symptom, there can also be further attribute information, such as: it the duration and lures Because etc. information；And it is directed to drug, it can have further attribute information, such as: the information such as single dose and inducement；And needle To disease, further attribute information equally can have, such as: diagnostic-type and property etc..

In addition, clinical information can also include: patient sign information, heredity medication history information, history of life information, check and examine Information etc..It is worth explanation, in the present embodiment, the not concrete form to the clinical information of patient and included specific Project is defined, clinical information may include be coherence check for characterizing the correlation properties of patient or being received, control The information such as treatment and drug.

Step 102 obtains clinical information corresponding concept node set in default knowledge of medication map.

After getting the clinical information of patient, available clinical information is corresponding in default knowledge of medication map Concept node set, wherein knowledge of medication map includes the relation information between concept node information and each concept node. Wherein, knowledge of medication map includes the correlation that concept node information can be the correlation properties for characterizing patient or be received The information such as inspection, treatment and drug, and the relation information between concept node then can be the pass of the drug between two drugs System, for example, both share will lead to allergy etc..

Fig. 5 is the flow diagram of the concept node set acquisition modes shown in the application second embodiment.Such as Fig. 5 institute Show, the concept node set acquisition modes in the present embodiment can specifically include:

Step 1021 determines entity type set corresponding to clinical information according to default classifying rules.

Step 1022 determines concept node set according to preset matching rule and entity type set.

Entity type set, entity class corresponding to clinical information are determined according to default classifying rules specifically, can be Entity type in type set is for characterizing patient attribute and/or drug attribute, then, further according to preset matching rule and in fact Body type set determines concept node set.

It is worth explanation, default classifying rules can be word segmentation regulation, the entity type collection got from clinical information It is combined into the set of keyword, such as: the set that the keywords such as male, flu, cough, Amoxicillin are constituted.And matching rule, It can be and carry out each concept node in each keyword and default knowledge of medication map in entity type set one by one Corresponding rule can be and be corresponded to by finding same or similar word.

In addition, after carrying out concept node and simply corresponding to, it can also be according to preset matching rule and entity type collection It closes and determines instantiation concept node set, determine that concept extends section according to instantiation concept node set and knowledge of medication map Point set, wherein concept node set includes that instantiated nodes set and concept extend node set.

Wherein, the determination for extending node set for above-mentioned concept can be according in instantiation concept node set It instantiates concept node and corresponding relation information obtains concept and extends node, node set is extended with product concept, wherein The relation information that concept extends node and instantiates between concept node is subordinate relation or inclusion relation.Fig. 9 is the application The data structure schematic diagram of Case knowledge subgraph shown in sixth embodiment.As shown in figure 9, the node of black is instantiation concept Node set, and the node of white is that concept extends node set.

It is step 103, true according to the relation information in concept node set and concept node set between each concept node Determine indicating risk information.

Clinical information is being obtained in default knowledge of medication map after corresponding concept node set, it can be according to concept Relation information in node set and concept node set between each concept node determines indicating risk information, wherein risk Prompt information is used to identify medicine and advises drug application risk in information.

Fig. 6 is the flow diagram of the indicating risk information method of determination shown in the application 3rd embodiment.Such as Fig. 6 institute Show, indicating risk information method of determination provided in this embodiment may include:

It is step 1031, true according to the relation information in concept node set and concept node set between each concept node Determine Case knowledge subgraph.

Risk relations information in step 1032, acquisition Case knowledge subgraph.

Step 1033 determines indicating risk information according to risk relations information.

It, can be according to the pass in concept node set and concept node set between each concept node with continued reference to Fig. 9 It is that information determines Case knowledge subgraph, then, obtains the risk relations information in Case knowledge subgraph, wherein risk relations letter Breath is disabling relationship or relationship is used with caution, to determine indicating risk information according to risk relations information.

Specifically, each concept node that can be in concept node set whether there is connection in knowledge of medication map Path (does not distinguish the direction on side), if it exists communication path, and the path corresponds to disabling relationship or relationship is used with caution, this output pair Answer being used with caution, disabling indicating risk for concept node, and if subordinate relation or inclusion relation are only existed in communication path, can may be used also Classification and the duplicate prompting of ingredient are advised to export corresponding concept node and medicine.

In addition, after determining indicating risk information, it can also be corresponding according to risk relations information, risk relations information Concept node and default words art mapping table export indicating risk information.Believed specifically, can be according to the indicating risk of output Breath and respective attributes, path node, relationship type, by inquiring formulation in advance, art mapping table generates indicating risk words Art.With continued reference to Fig. 9, so that it may advise the relation path of the drug 1 in information and the drug 1 in allergies information to generate according to medicine Indicating risk talks about art: " it include drug ingedient and allergies drug 1 is similar drugs that medicine, which advises drug 1, therefore there may be crossing It is quick ".

In the present embodiment, by first obtaining the clinical information of patient, then by clinical information and default knowledge of medication map In concept node corresponded to, to obtain corresponding concept node set, then further according to concept node set and concept Relation information in node set between each concept node determines indicating risk information, thus to medicine advise in drug using carry out Indicating risk.Also, the method for carrying out indicating risk using knowledge of medication map, can be more preferable with the carry out of patient clinical information Matching, optimize indicating risk toggle rate, and according to relation information generate indicating risk information by way of, enhance wind The interpretation nearly prompted.

On the basis of the above embodiments, for the generation of knowledge of medication map, it can be and generated according to medical data source Default knowledge of medication map, is illustrated below by exemplary embodiment.

Fig. 7 is the flow diagram of the knowledge of medication map generating mode shown in the application fourth embodiment.Such as Fig. 7 institute Show, drug knowledge mapping generating mode in the present embodiment, comprising:

Step 201, basis preset concept keyword and each concept key in knowledge format acquisition medical data source Relation keyword between word.

Specifically, first the sentence in medical data source can be segmented, then passed through by preset segmenting method Obtain the relation keyword between the concept keyword and each concept keyword in medical data source.For example, get Concept keyword is " fever ", " headache ", " abdominal distension ", " Amoxicillin " and " streptomysin " etc., and concept keyword " A Moxi Relation keyword between woods " and " streptomysin " can be " being used with caution ".

Fig. 8 is the flow diagram that the application the 5th implements the keyword acquisition modes exemplified.As shown in figure 8, this reality Apply keyword acquisition modes in example, comprising:

Step 2011 obtains concept keyword according to default knowledge format from primary sources library.

Step 2012 obtains relationship key according to default knowledge format from primary sources library and secondary sources library Word.

It is worth understanding, concept keyword is obtained from primary sources library according to default knowledge format, is known according to default Know format and obtain relation keyword from primary sources library and secondary sources library, wherein presets in primary sources library First confidence level is greater than default second confidence level in secondary sources library.

Specifically, primary sources library can be the higher database of technorati authority, such as structural data, such as: Bureau of Drugs Supervision Ratify the Drug catalogue of listing, the ICD terminology standard for defending strong committee's publication etc..And secondary sources can for semi-structured data or Person's Un-structured data, such as: pharmacopeia catalogue, data directory, standard literature title and keyword and package insert part Field etc..

Since primary sources library data structure is simple, and it is authoritative high, and word specification, therefore, most of type can It can be configured to extraction and more new technological process by simple rule, suitable for acquisition concept keyword.And secondary sources Library data source is various, can increase the source of data, be more suitable for obtaining relation keyword.

Step 202 generates default knowledge of medication map according to concept keyword and relation keyword.

And after generating knowledge of medication map, it can also determine the confidence of knowledge information in default knowledge of medication map Degree, wherein knowledge information is concept node information or relation information, and the first knowledge information is incorporated to default knowledge of medication map, Wherein, the first knowledge information is the knowledge information that knowledge confidence is greater than or equal to default knowledge confidence, for example, knowledge is set Knowledge information of the reliability more than or equal to 0.95 is incorporated to default knowledge of medication map.Be worth explanation, according to knowledge information come The quantity of the database in default confidence level and knowledge information the institute source of the database in source determines knowledge confidence.For knowledge The data source in information institute source is all preset with data source confidence value, and authoritative higher database, default confidence level Also higher, in addition, the quantity of the database for knowledge information institute source, source is more, then illustrates the knowledge information confidence level It is higher, it can the determination of confidence level is carried out based on source authority and the mode of multi-source cross validation.

In addition, evaluation information can also be obtained after the confidence level of knowledge information in determining default knowledge of medication map, Wherein, evaluation information is used to evaluate the reliability of the second knowledge information, and the second knowledge information is that knowledge confidence is known less than default The knowledge information for knowing confidence level, determines whether for the second knowledge information to be incorporated to default knowledge of medication map according to evaluation information.

Knowledge information set to be verified is obtained, the knowledge information to be verified of knowledge information set to be verified is the second knowledge letter Breath determines according to the knowledge information to be verified in knowledge information set to be verified and gathers whole accuracy rate, and set is whole accurate The knowledge information collection to be verified that rate is greater than or equal to default accuracy rate is merged into default knowledge of medication map.It is worth understanding, it can To be for being to be incorporated to the knowledge information set to be verified of knowledge of medication map to be verified.Wherein it is possible to for the set into The whole verification of row, so that the whole accuracy rate for the set is obtained, if whole accuracy rate is greater than 0.95, then it is assumed that set inspection It tests and passes through, so that the collection is merged into default knowledge of medication map.

But be, for example, less than 0.95 if gathering whole accuracy rate is less than default accuracy rate, then it is small will to gather whole accuracy rate Multiple knowledge information subclass to be verified are split as in the knowledge information set to be verified of default accuracy rate, are known further according to be verified The knowledge information to be verified known in information subset conjunction determines subclass entirety accuracy rate, then, subclass entirety accuracy rate is big Default knowledge of medication map is merged into or equal to the knowledge information subset to be verified of default accuracy rate.It is worth understanding, it can be with The big collection that whole accuracy rate is less than default accuracy rate is being subjected to segmentation verifying, subclass entirety accuracy rate is being greater than default Accuracy rate subset is merged into default knowledge of medication map, to guarantee the reliability of knowledge information in knowledge of medication map.

In addition, on the basis of the above embodiments, according to each concept in concept node set and concept node set After relation information between node determines indicating risk information, indicating risk information can also be exported for doctor, such as: " medicine It is similar drugs that advise drug 1, which include drug ingedient and allergies drug 1, therefore there may be cross allergies ".And it is got in doctor After indicating risk information, it can choose " reception " and advised with modifying the medicine oneself opened, select " to refuse after can also being confirmed It is advised with the medicine for continuing to be opened before submitting absolutely ".

After doctor feeds back, available feedback command, feedback command is to receive instruction or refusal instruction, is connect By instruction for receiving indicating risk information, refusal instruction is for refusing indicating risk information.

And when feedback command is to receive instruction, the knowledge confidence of knowledge information is increased by the first numerical value, for example, can be with It is increase by 0.01, when feedback command is refusal instruction, the knowledge confidence of knowledge information is reduced into second value, for example, It can be reduction 0.01.If the knowledge confidence of knowledge information is less than default knowledge confidence, invalid knowledge information, if knowing When knowing the knowledge confidence of information and being greater than or equal to default knowledge confidence, then activated knowledge information.

For the building mode of knowledge of medication map in through this embodiment, can be led to less knowledge on drug abuse construction work The building of extensive, low-density knowledge relation is converted into small rule by the building for crossing the concept hierarchy, inclusion relation in knowledge mapping Mould, the building of highdensity knowledge relation, increase automatic Verification mechanism, reduce manual examination and verification cost.Also, in conjunction with knowledge of medication figure The multi-source of knowledge information excavates in spectrum, and updating across number for knowledge may be implemented.In addition, passing through structure compared to rule matching method The scheme that knowledge of medication map can be connect by the conceptual relation of multistratum classification with example is built, it can be with patient clinical information's It is preferably matched, optimizes indicating risk toggle rate, and by way of generating indicating risk information according to relation information, Enhance the interpretation of indicating risk.

In order to advise indicating risk method to be illustrated in actual application scenarios medicine provided herein, below may be used To be illustrated by advising indicating risk method to be applied in an illustrative scene medicine.

It can be applied to medical scene, patient diagnosed in doctor, and medicine is outputed for patient by medical terminal and is advised Later, server obtains the clinical information of patient.Figure 10 is that the medicine that the application the 7th implements to exemplify advises indicating risk method Flow diagram.As shown in Figure 10, medicine provided in this embodiment advises indicating risk method, comprising:

Step 301 carries out knowledge information excavation to medical data source data.

Step 302, knowledge information verification and audit.

Step 303 generates knowledge mapping.

Step 304, the clinical information for obtaining patient.

Step 305, corresponding concept node set.

Step 306 determines Case knowledge subgraph.

Step 307 determines indicating risk information.

Step 308 generates prompt words art information.

Step 309 obtains feedback command.

Figure 11 is the flow diagram that the medicine that the application the 8th implements to exemplify advises indicating risk device.As shown in figure 11, Medicine provided in this embodiment advises indicating risk device 400, comprising:

Data obtaining module 401, for obtaining the clinical information of patient, the clinical information includes that medicine advises information and diagnosis Information；

Information link block 402, for obtaining the clinical information corresponding concept section in default knowledge of medication map Point set, the knowledge of medication map include the relation information between concept node information and each concept node；

Indicating risk module 403, for according to each concept in the concept node set and the concept node set Relation information between node determines indicating risk information, and the indicating risk information is used to identify the medicine and advises medicine in information Object application risk.

In a kind of possible design, the information link block 402 is specifically used for:

According to default classifying rules determine the clinical information corresponding to entity type set, the entity type set In entity type for characterizing patient attribute and/or drug attribute；

The concept node set is determined according to preset matching rule and the entity type set.

In a kind of possible design, the information link block 402 is specifically used for:

Instantiation concept node set is determined according to the preset matching rule and the entity type set；

Determine that concept extends node set, institute according to the instantiation concept node set and the knowledge of medication map Stating concept node set includes that the instantiated nodes set and the concept extend node set.

In a kind of possible design, the information link block 402 is specifically used for:

It is obtained according to the instantiation concept node instantiated in concept node set and corresponding relation information general It reads and extends node, extend node set to generate the concept, wherein the concept extends node and the instantiation concept section The relation information between point is subordinate relation or inclusion relation.

In a kind of possible design, the indicating risk module 403 is specifically used for:

It is true according to the relation information in the concept node set and the concept node set between each concept node Determine Case knowledge subgraph；

Obtain the risk relations information in the Case knowledge subgraph, wherein the risk relations information is disabling relationship Or relationship is used with caution；

The indicating risk information is determined according to the risk relations information.

On the basis of embodiment illustrated in fig. 11, Figure 12 is that the medicine that the application the 9th implements to exemplify advises indicating risk device Flow diagram.As shown in figure 12, medicine provided in this embodiment advises indicating risk device 400, further includes:

Art output module 405 is talked about, for according to the risk relations information, the corresponding concept section of the risk relations information Point and default words art mapping table export the indicating risk information.

In a kind of possible design, the clinical information further includes patient physiological information, symptom information and patient's mistake At least one of quick history information.

In a kind of possible design, described device, further includes:

Map generation module 404, for generating the default knowledge of medication map according to medical data source.

In a kind of possible design, the map generation module 404 is also used to:

According to default knowledge format obtain concept keyword in the medical data source and each concept keyword it Between relation keyword；

The default knowledge of medication map is generated according to the concept keyword and the relation keyword, wherein institute Stating concept node information includes the concept keyword, and the relation information includes the relation keyword.

In a kind of possible design, the map generation module 404 is specifically used for:

The concept keyword is obtained from primary sources library according to the default knowledge format；

The relationship is obtained from the primary sources library and secondary sources library according to the default knowledge format Keyword, wherein default first confidence level in the primary sources library is greater than default the second of the secondary sources library and sets Reliability.

In a kind of possible design, the map generation module 404 is specifically used for:

Determine the confidence level of knowledge information in the default knowledge of medication map, wherein the knowledge information is described general Read nodal information or the relation information；

First knowledge information is incorporated to the default knowledge of medication map, wherein first knowledge information is set for knowledge Reliability is greater than or equal to the knowledge information of default knowledge confidence.

On the basis of embodiment illustrated in fig. 12, Figure 13 is that the medicine that the application the tenth implements to exemplify advises indicating risk device Flow diagram.As shown in figure 13, medicine provided in this embodiment advises indicating risk device 400, further includes: evaluation obtains module 406；

The evaluation obtains module 406, is specifically used for:

Evaluation information is obtained, the evaluation information is used to evaluate the reliability of the second knowledge information, the second knowledge letter Breath is the knowledge information that knowledge confidence is less than the default knowledge confidence；

Determined whether for second knowledge information to be incorporated to the default knowledge of medication map according to the evaluation information.

In a kind of possible design, the map generation module 404 is also used to:

According to the default confidence level of the database in knowledge information institute source and the number in knowledge information institute source The knowledge confidence is determined according to the quantity in library.

In a kind of possible design, described device, further includes: knowledge check module 407；

The knowledge check module 407, is specifically used for:

Obtain knowledge information set to be verified, the knowledge information to be verified of the knowledge information set to be verified is described the Two knowledge informations；

It is determined according to the knowledge information to be verified in the knowledge information set to be verified and gathers whole accuracy rate；

The knowledge information collection to be verified that the whole accuracy rate of the set is greater than or equal to default accuracy rate is merged into described Default knowledge of medication map.

In a kind of possible design, the knowledge check module 407 is also used to:

The knowledge information set to be verified that the whole accuracy rate of the set is less than the default accuracy rate is split as multiple Knowledge information subclass to be verified；

Subclass entirety accuracy rate is determined according to the knowledge information to be verified in the knowledge information subclass to be verified；

The knowledge information subset to be verified that the subclass entirety accuracy rate is greater than or equal to default accuracy rate is merged into The default knowledge of medication map.

On the basis of embodiment illustrated in fig. 13, Figure 14 is that the medicine that the application the 11st implements to exemplify advises indicating risk to fill The flow diagram set.As shown in figure 14, medicine provided in this embodiment advises indicating risk device, further includes: feedback obtains module 408；

The feedback obtains module 408, and for obtaining feedback command, the feedback command is to receive instruction or refuse to refer to It enables, the receiving instruction is for receiving the indicating risk information, and the refusal instruction is for refusing the indicating risk information.

In a kind of possible design, described device, further includes: knowledge evaluation module 409；

The knowledge evaluation module 409, is specifically used for:

When the feedback command is to receive instruction, the knowledge confidence of knowledge information is increased by the first numerical value, when described When feedback command is refusal instruction, the knowledge confidence of knowledge information is reduced into second value；

If the knowledge confidence of the knowledge information is less than default knowledge confidence, the invalid knowledge information, if When the knowledge confidence of the knowledge information is greater than or equal to the default knowledge confidence, then the knowledge information is activated.

It is worth explanation, the medicine that Figure 11-embodiment illustrated in fig. 14 provides advises indicating risk device, can be used for executing above-mentioned Method provided by embodiment of anticipating, specific implementation is similar with technical effect, is not discussed here.

According to an embodiment of the present application, present invention also provides a kind of electronic equipment and a kind of readable storage medium storing program for executing.

Figure 15 is for realizing that the medicine of the embodiment of the present application advises the block diagram of the electronic equipment of indicating risk method.Such as Figure 15 institute Show, electronic equipment is intended to indicate that various forms of digital computers, such as, laptop computer, desktop computer, workbench, Personal digital assistant, server, blade server, mainframe computer and other suitable computer.Electronic equipment can be with Indicate various forms of mobile devices, such as, personal digital assistant, cellular phone, smart phone, wearable device and other classes As computing device.Component, their connection and relationship shown in this article and their function are merely exemplary, and It is not intended to limit the realization of the application that is described herein and/or requiring.

As shown in figure 15, which includes: one or more processors 501, memory 502, and for connecting The interface of each component, including high-speed interface and low-speed interface.All parts are interconnected using different bus, and can be by It is mounted on public mainboard or installs in other ways as needed.Processor can instruction to executing in electronic equipment Handled, including storage in memory or on memory (such as, to be coupled to interface in external input/output device Display equipment) on show GUI graphical information instruction.In other embodiments, if desired, can be by multiple processors And/or multiple bus is used together with multiple memories with multiple memories.It is also possible to multiple electronic equipments are connected, it is each Equipment provides the necessary operation in part (for example, as server array, one group of blade server or multiprocessor system System).In Figure 15 by taking a processor 501 as an example.

Memory 502 is non-transitory computer-readable storage medium provided herein.Wherein, the memory is deposited The instruction that can be executed by least one processor is contained, is advised so that at least one described processor executes medicine provided herein Indicating risk method.The non-transitory computer-readable storage medium of the application stores computer instruction, which is used for So that computer is executed medicine provided herein and advises indicating risk method.

Memory 502 is used as a kind of non-transitory computer-readable storage medium, can be used for storing non-instantaneous software program, non- Instantaneous computer executable program and module, as the medicine in the embodiment of the present application advises the corresponding program of indicating risk method to refer to Order/module (for example, data obtaining module 401, information link block 402 shown in attached drawing 11 and indicating risk module 403). Non-instantaneous software program, instruction and the module that processor 501 is stored in memory 502 by operation, thereby executing service The various function application and data processing of device, i.e. medicine in realization above method embodiment advise indicating risk method.

Memory 502 may include storing program area and storage data area, wherein storing program area can store operation system Application program required for system, at least one function；Storage data area can be stored to be set according to electronics provided by the embodiments of the present application Standby uses created data etc..In addition, memory 502 may include high-speed random access memory, it can also include non- Volatile storage, for example, at least a disk memory, flush memory device or other non-instantaneous solid-state memories.Some In embodiment, it includes the memory remotely located relative to processor 501 that memory 502 is optional, these remote memories can be with Electronic equipment is provided by network connection to the embodiment of the present application.The example of above-mentioned network includes but is not limited to internet, enterprise Intranet, local area network, mobile radio communication and combinations thereof.

It can also include: input unit 503 and output device 504 that the embodiment of the present application, which provides electronic equipment,.Processor 501, memory 502, input unit 503 and output device 504 can be connected by bus or other modes, with logical in Figure 15 It crosses for bus connection.

Input unit 503 can receive the number or character information of input, and generates and provide electronics with the embodiment of the present application The related key signals input of the user setting and function control of equipment, such as touch screen, keypad, mouse, track pad, touch The input units such as plate, indicating arm, one or more mouse button, trace ball, control stick.Output device 504 may include showing Show equipment, auxiliary lighting apparatus (for example, LED) and haptic feedback devices (for example, vibrating motor) etc..The display equipment can wrap It includes but is not limited to, liquid crystal display (LCD), light emitting diode (LED) display and plasma scope.In some embodiment party In formula, display equipment can be touch screen.

The various embodiments of system and technology described herein can be in digital electronic circuitry, integrated circuit system It is realized in system, dedicated ASIC (specific integrated circuit), computer hardware, firmware, software, and/or their combination.These are various Embodiment may include: to implement in one or more computer program, which can be It executes and/or explains in programmable system containing at least one programmable processor, which can be dedicated Or general purpose programmable processors, number can be received from storage system, at least one input unit and at least one output device According to and instruction, and data and instruction is transmitted to the storage system, at least one input unit and this at least one output Device.

These calculation procedures (also referred to as program, software, software application or code) include the machine of programmable processor Instruction, and can use programming language, and/or the compilation/machine language of level process and/or object-oriented to implement these Calculation procedure.As used herein, term " machine readable media " and " computer-readable medium " are referred to for referring to machine It enables and/or data is supplied to any computer program product, equipment, and/or the device of programmable processor (for example, disk, light Disk, memory, programmable logic device (PLD)), including, receive the machine readable of the machine instruction as machine-readable signal Medium.Term " machine-readable signal " is referred to for machine instruction and/or data to be supplied to any of programmable processor Signal.

In order to provide the interaction with user, system and technology described herein, the computer can be implemented on computers The display device for showing information to user is included (for example, CRT (cathode-ray tube) or LCD (liquid crystal display) monitoring Device)；And keyboard and indicator device (for example, mouse or trace ball), user can by the keyboard and the indicator device come Provide input to computer.The device of other types can be also used for providing the interaction with user；For example, being supplied to user's Feedback may be any type of sensory feedback (for example, visual feedback, audio feedback or touch feedback)；And it can use Any form (including vocal input, voice input or tactile input) receives input from the user.

System described herein and technology can be implemented including the computing system of background component (for example, as data Server) or the computing system (for example, application server) including middleware component or the calculating including front end component System is (for example, the subscriber computer with graphic user interface or web browser, user can pass through graphical user circle Face or the web browser to interact with the embodiment of system described herein and technology) or including this backstage portion In any combination of computing system of part, middleware component or front end component.Any form or the number of medium can be passed through Digital data communicates (for example, communication network) and is connected with each other the component of system.The example of communication network includes: local area network (LAN), wide area network (WAN) and internet.

Computer system may include client and server.Client and server is generally off-site from each other and usually logical Communication network is crossed to interact.By being run on corresponding computer and each other with the meter of client-server relation Calculation machine program generates the relationship of client and server.

According to the technical solution of the embodiment of the present application, by first obtaining the clinical information of patient, then by clinical information and pre- If the concept node in knowledge of medication map is corresponded to, to obtain corresponding concept node set, then further according to concept section Relation information in point set and concept node set between each concept node determines indicating risk information, thus in advising to medicine Drug using carry out indicating risk.Also, the method for carrying out indicating risk using knowledge of medication map, can be with patient clinical The progress of information preferably matches, and optimizes indicating risk toggle rate, and by generating indicating risk information according to relation information Mode, enhance the interpretation of indicating risk.

It should be understood that various forms of processes illustrated above can be used, rearrangement increases or deletes step.Example Such as, each step as described in this application can be performed in parallel or be sequentially performed the order that can also be different and execute, only It is desired as a result, being not limited herein to can be realized technical solution disclosed in the present application.

Above-mentioned specific embodiment does not constitute the limitation to the application protection scope.Those skilled in the art should be bright White, according to design requirement and other factors, various modifications can be carried out, combination, sub-portfolio and substitution.It is any in the application Spirit and principle within made modifications, equivalent substitutions and improvements etc., should be included within the application protection scope.