阅读说明：本技术 胸腔镜肺癌切除术中转开胸风险诊断预测模型及构建系统 (Breast risk diagnosis prediction model in thoracoscopic lung cancer resection and construction system ) 是由 周健 林锋 刘伦旭 于 2021-07-27 设计创作，主要内容包括：本发明属于手术风险诊断预测模型技术领域,公开了一种胸腔镜肺癌切除术中转开胸风险诊断预测模型及构建系统,所述胸腔镜肺癌切除术中转开胸风险诊断预测模型的构建系统包括：数据获取模块、风险诊断模型构建模块、数据预处理模块、数据融合模块、数据集训练模块、模型验证与优化模块、中央控制模块、风险诊断预测模块、数据存储模块、更新显示模块。本发明基于华西医院胸外科上万例数据,以经典的逻辑回归、人工神经网络和随机森林等机器学习算法,建立肺癌胸腔镜手术中转开胸的风险诊断预测模型,并基于建立的网页版nomogram图,输入一些指标后,即可实现在线预测胸腔镜肺癌切除术中转开胸风险的大小。(The invention belongs to the technical field of operation risk diagnosis prediction models, and discloses a thoracotomy risk diagnosis prediction model in thoracoscopic lung cancer resection and a construction system thereof, wherein the construction system of the thoracotomy risk diagnosis prediction model in thoracoscopic lung cancer resection comprises: the system comprises a data acquisition module, a risk diagnosis model construction module, a data preprocessing module, a data fusion module, a data set training module, a model verification and optimization module, a central control module, a risk diagnosis prediction module, a data storage module and an update display module. The invention is based on tens of thousands of data of thoracic surgery of western China hospital, and based on the machine learning algorithms of classical logistic regression, artificial neural network, random forest and the like, a risk diagnosis and prediction model of thoracotomy in the thoracoscopic lung cancer surgery is established, and based on the established webpage version nomogram graph, after some indexes are input, the online prediction of the thoracotomy risk in the thoracoscopic lung cancer resection can be realized.)

1. A construction system of a prediction model for diagnosing the risk of thoracotomy in thoracoscopic lung cancer resection is characterized by comprising the following components:

the risk diagnosis model building module is connected with the central control module and used for building a thoracotomy risk diagnosis prediction model in thoracoscopic lung cancer resection through a model building program based on a machine learning algorithm including classical logistic regression, an artificial neural network and a random forest;

the data fusion module is connected with the central control module and used for carrying out fusion processing on various types of preprocessed data information of the clinical patient through a data fusion program to obtain a risk diagnosis prediction data set and dividing the data set into a sample set and a test set;

the data set training module is connected with the central control module and used for inputting the sample set into the risk diagnosis prediction model for training and learning through a data set training program and inputting the test set into the risk diagnosis prediction model for testing after training;

the central control module is connected with the risk diagnosis model construction module, the data fusion module, the data set training module and the risk diagnosis prediction module and is used for coordinately controlling the normal operation of each module of the construction system for opening the chest risk diagnosis prediction model in the thoracoscopic lung cancer resection through a central processing unit and/or a single chip microcomputer;

the risk diagnosis and prediction module is connected with the central control module and used for diagnosing and predicting the chest opening risk in the thoracoscopic lung cancer resection of the clinical patient through the chest opening risk diagnosis and prediction model in the thoracoscopic lung cancer resection and calculating the comprehensive risk value, and comprises the following steps:

acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

extracting index data based on the acquired image data; after the acquired image information is processed, whether a tumor exists in the image is judged by analyzing the processed image information, and if the tumor exists, whether the size of the tumor exceeds a preset threshold value is judged; meanwhile, judging whether the lymph nodes in the image have tumors, whether the tumors are immersed in blood vessels and the degree of adhesion of the thoracic cavity, and obtaining an analysis judgment result of the image;

calculating to generate corresponding clinical indexes based on the extracted index data, the obtained body characteristic data of the clinical patient, the image information, the risk prediction indexes, the gene information, the pathological benign and malignant label, the diagnosis result data given by a clinician and the analysis and judgment result of the image, and inputting the corresponding clinical indexes into a risk diagnosis prediction model for risk prediction;

the risk diagnosis prediction model outputs risk probability corresponding to each clinical index according to each input clinical index, and calculates the operation risk comprehensive probability value of the clinical patient;

wherein, the operation risk comprehensive probability value of the clinical patient is calculated by adopting the following formula:

wherein the parameter n represents the number of clinical indicators input to the risk diagnosis prediction model, R_iRepresents the operation risk probability predicted by a clinical index through a risk diagnosis prediction model, W_iRepresents a weighted impact factor of the ith clinical indicator on surgical risk.

2. The system for constructing a prediction model for diagnosis of risk of thoracotomy in thoracoscopic lung cancer resection according to claim 1, wherein the system for constructing a prediction model for diagnosis of risk of thoracotomy in thoracoscopic lung cancer resection further comprises:

the data acquisition module is connected with the central control module and used for acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

the data preprocessing module is connected with the central control module and used for carrying out noise reduction and normalization processing on the acquired data through a data preprocessing program and marking and dividing an interested area corresponding to a pathological result on the image;

the model verification and optimization module is connected with the central control module and is used for evaluating the effectiveness, the generalization and the robustness of the model under different parameters and scenes and performing iterative optimization on the performance of the model by adopting a reward and punishment mechanism;

the central control module is connected with the data acquisition module, the data preprocessing module, the model verification and optimization module, the data storage module and the update display module and is used for coordinately controlling the normal operation of each module of the construction system of the open chest risk diagnosis and prediction model in the thoracoscopic lung cancer resection through a central processing unit and/or a single chip microcomputer;

the data storage module is connected with the central control module and used for storing various acquired data information of clinical patients, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the risk diagnosis prediction result through the memory;

and the updating display module is connected with the central control module and is used for updating and displaying various acquired data information of the clinical patient, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the real-time data of the risk diagnosis prediction result through the display.

3. The system for constructing a prediction model for diagnosing the risk of thoracotomy in thoracoscopic lung cancer as recited in claim 2, wherein the obtaining of the risk prediction index in the data obtaining module comprises:

(1) acquiring the plurality of clinical indexes of clinical patients through professional medical equipment;

(2) after the integrity and the quality of the data of the plurality of clinical indexes are verified and evaluated, desensitization processing is carried out on the data, and privacy information and medical institution information of patients are hidden;

(3) and taking a plurality of clinical indexes with the clinical evaluation indexes higher than the first threshold value as prediction indexes of the risk of thoracotomy in thoracoscopic lung cancer resection of the clinical patient.

4. The system of claim 2, wherein the data preprocessing module is configured to search for a lesion corresponding to a pathological result by a physician, and then perform contour segmentation and local block segmentation on the lesion by an automatic segmentation method modified and confirmed by the physician or by a manual segmentation method by the physician.

5. The system for constructing an open chest risk diagnosis prediction model in thoracoscopic lung cancer resection of claim 1, wherein in the data set training module, the inputting of the sample set into the risk diagnosis prediction model through the data set training program for training and learning comprises:

(1) acquiring training data and training the risk diagnosis prediction model according to the training data to obtain a first training model;

(2) obtaining a second training model according to part of parameters of the first training model;

(3) and training the second training model according to the loss function between the first training model and the second training model to obtain a risk diagnosis prediction model.

6. The system for constructing a prediction model for open chest risk diagnosis in thoracoscopic lung cancer resection of claim 5, wherein the step of training the prediction model for risk diagnosis according to the training data to obtain a first training model comprises:

preprocessing training data to obtain a text sequence and label data corresponding to the training data;

performing probability calculation processing on the text sequence and the label data according to a preset model to obtain a probability value, wherein the probability value represents the corresponding relation between a word vector corresponding to the text sequence and the label data;

and training the risk diagnosis prediction model according to the probability value to obtain a first training model.

7. The system for constructing a prediction model for diagnosing the risk of developing thoracotomy in thoracoscopic lung cancer as recited in claim 1, wherein said extracting index data based on the acquired image data comprises:

performing character region filtering processing on the medical image digital image to obtain a binary image after character region filtering; removing isolated points from the binary image to obtain an updated binary image;

scanning the obtained updated binary image in the horizontal direction and the vertical direction to obtain the size of each binary medical image and position information in the binary image;

dividing each binary medical image into four equal parts according to the form of width and height half, and respectively performing line scanning and column scanning on each equal part to respectively obtain the height and width of a character area in the equal part; obtaining the size and position information of all character areas of four equal parts in each binary medical image;

and extracting the character areas of four equal parts in each binary medical image.

8. An information data processing terminal, characterized in that the information data processing terminal is used for realizing a construction system of an open chest risk diagnosis prediction model in thoracoscopic lung cancer resection according to any one of claims 1-7.

9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for applying a system for constructing a predictive model for diagnosing risk of thoracotomy in thoracoscopic lung cancer as recited in any one of claims 1-7 when executed on an electronic device.

10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to apply a system for constructing a prediction model for diagnosis of risk of thoracotomy in thoracoscopic lung cancer resection according to any one of claims 1 to 7.

Technical Field

The invention belongs to the technical field of operation risk diagnosis and prediction, and particularly relates to a prediction model and a construction system for thoracotomy risk diagnosis in thoracoscopic lung cancer resection.

Background

At present, the thoracoscopic surgery is developed in the early 90 s of the 20 th century, and through the development of twenty years, the safety and the effectiveness of the thoracoscopic lung cancer radical surgery for treating I, II-stage non-small cell lung cancer are determined, and the thoracoscopic lung cancer radical surgery has the advantages of small wound, quick recovery, light postoperative pain, small shoulder joint function influence and the like.

The surgical approach of the thoracoscope lung cancer radical treatment is changed by the auxiliary thoracoscope surgery under the incision, three holes, a single operation hole and a single hole, and is increasingly minimally invasive and refined. At present, the operation of thoracoscopic lung cancer radical operation under a single hole, namely the only hole, is achieved in a few thoracic surgery centers, the number of incisions is further reduced, postoperative pain is relieved, operative wounds are reduced, and the great advantage of minimally invasive surgery is reflected. In thoracoscopic lung cancer resection, thoracotomy may be transferred due to reasons of massive hemorrhage, close pleura adhesion and the like, but the prior art cannot realize accurate prediction of thoracotomy risk diagnosis in thoracoscopic lung cancer resection. Therefore, a diagnosis and prediction model for the risk of thoracotomy in thoracoscopic lung cancer resection and a construction system thereof are needed.

Through the above analysis, the problems and defects of the prior art are as follows: the prior art can not realize accurate prediction of open chest risk diagnosis in thoracoscopic lung cancer resection.

The difficulty in solving the above problems and defects is:

(1) in thoracoscopic lung cancer resection, the chest may be transferred due to massive hemorrhage, close adhesion of pleura and the like, but it is unclear how to predict whether the chest will be transferred before operation.

(2) Some complications inevitably occur after thoracoscopic lung cancer resection, continuous air leakage is the most common complication, the occurrence rate is unequal to 8% -30%, how to predict the occurrence of the complications before operation, identify high-risk groups, and perform early intervention is unclear.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection and a construction system.

The invention is realized in this way, a construction system of a prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection, the construction system of the prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection comprises:

the risk diagnosis model building module is connected with the central control module and used for building a thoracotomy risk diagnosis prediction model in thoracoscopic lung cancer resection through a model building program based on a machine learning algorithm including classical logistic regression, an artificial neural network and a random forest;

the data fusion module is connected with the central control module and used for carrying out fusion processing on various types of preprocessed data information of the clinical patient through a data fusion program to obtain a risk diagnosis prediction data set and dividing the data set into a sample set and a test set;

the data set training module is connected with the central control module and used for inputting the sample set into the risk diagnosis prediction model for training and learning through a data set training program and inputting the test set into the risk diagnosis prediction model for testing after training;

the central control module is connected with the risk diagnosis model construction module, the data fusion module, the data set training module and the risk diagnosis prediction module and is used for coordinately controlling the normal operation of each module of the construction system for opening the chest risk diagnosis prediction model in the thoracoscopic lung cancer resection through a central processing unit and/or a single chip microcomputer;

the risk diagnosis and prediction module is connected with the central control module and used for diagnosing and predicting the chest opening risk in the thoracoscopic lung cancer resection of the clinical patient through the chest opening risk diagnosis and prediction model in the thoracoscopic lung cancer resection and calculating the comprehensive risk value, and comprises the following steps:

acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

extracting index data based on the acquired image data; after the acquired image information is processed, whether a tumor exists in the image is judged by analyzing the processed image information, and if the tumor exists, whether the size of the tumor exceeds a preset threshold value is judged; meanwhile, judging whether the lymph nodes in the image have tumors, whether the tumors are immersed in blood vessels and the degree of adhesion of the thoracic cavity, and obtaining an analysis judgment result of the image;

calculating to generate corresponding clinical indexes based on the extracted index data, the obtained body characteristic data of the clinical patient, the image information, the risk prediction indexes, the gene information, the pathological benign and malignant label, the diagnosis result data given by a clinician and the analysis and judgment result of the image, and inputting the corresponding clinical indexes into a risk diagnosis prediction model for risk prediction;

the risk diagnosis prediction model outputs risk probability corresponding to each clinical index according to each input clinical index, and calculates the operation risk comprehensive probability value of the clinical patient;

wherein, the operation risk comprehensive probability value of the clinical patient is calculated by adopting the following formula:

wherein the parameter n represents the number of clinical indicators input to the risk diagnosis prediction model, R_iRepresents the operation risk probability predicted by a clinical index through a risk diagnosis prediction model, W_iRepresents the ith clinical index toWeighted impact factors of surgical risk.

Further, the system for constructing the open chest risk diagnosis prediction model in thoracoscopic lung cancer resection further comprises:

the data acquisition module is connected with the central control module and used for acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

the data preprocessing module is connected with the central control module and used for carrying out noise reduction and normalization processing on the acquired data through a data preprocessing program and marking and dividing an interested area corresponding to a pathological result on the image;

the model verification and optimization module is connected with the central control module and is used for evaluating the effectiveness, the generalization and the robustness of the model under different parameters and scenes and performing iterative optimization on the performance of the model by adopting a reward and punishment mechanism;

the central control module is connected with the data acquisition module, the data preprocessing module, the model verification and optimization module, the data storage module and the update display module and is used for coordinately controlling the normal operation of each module of the construction system of the open chest risk diagnosis and prediction model in the thoracoscopic lung cancer resection through a central processing unit and/or a single chip microcomputer;

the data storage module is connected with the central control module and used for storing various acquired data information of clinical patients, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the risk diagnosis prediction result through the memory;

and the updating display module is connected with the central control module and is used for updating and displaying various acquired data information of the clinical patient, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the real-time data of the risk diagnosis prediction result through the display.

Further, in the data obtaining module, the obtaining of the risk prediction index includes:

(1) acquiring the plurality of clinical indexes of clinical patients through professional medical equipment;

(2) after the integrity and the quality of the data of the plurality of clinical indexes are verified and evaluated, desensitization processing is carried out on the data, and privacy information and medical institution information of patients are hidden;

(3) and taking a plurality of clinical indexes with the clinical evaluation indexes higher than the first threshold value as prediction indexes of the risk of thoracotomy in thoracoscopic lung cancer resection of the clinical patient.

Furthermore, in the data preprocessing module, a doctor searches for a lesion corresponding to a pathological result, and then the lesion is subjected to contour segmentation and local block segmentation by using an automatic segmentation method modified and confirmed by the doctor or a manual segmentation method adopted by the doctor.

Further, in the data set training module, the inputting a sample set into the risk diagnosis prediction model for training and learning through a data set training program includes:

(1) acquiring training data and training the risk diagnosis prediction model according to the training data to obtain a first training model;

(2) obtaining a second training model according to part of parameters of the first training model;

(3) and training the second training model according to the loss function between the first training model and the second training model to obtain a risk diagnosis prediction model.

Further, the step of training the risk diagnosis prediction model according to the training data to obtain a first training model includes:

preprocessing training data to obtain a text sequence and label data corresponding to the training data;

performing probability calculation processing on the text sequence and the label data according to a preset model to obtain a probability value, wherein the probability value represents the corresponding relation between a word vector corresponding to the text sequence and the label data;

and training the risk diagnosis prediction model according to the probability value to obtain a first training model.

Further, the extracting of the index data based on the acquired image data includes:

performing character region filtering processing on the medical image digital image to obtain a binary image after character region filtering; removing isolated points from the binary image to obtain an updated binary image;

scanning the obtained updated binary image in the horizontal direction and the vertical direction to obtain the size of each binary medical image and position information in the binary image;

dividing each binary medical image into four equal parts according to the form of width and height half, and respectively performing line scanning and column scanning on each equal part to respectively obtain the height and width of a character area in the equal part; obtaining the size and position information of all character areas of four equal parts in each binary medical image;

and extracting the character areas of four equal parts in each binary medical image.

The invention also aims to provide an information data processing terminal, which is characterized in that the information data processing terminal is used for realizing a construction system of the open chest risk diagnosis prediction model in thoracoscopic lung cancer resection.

It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for applying a system for constructing a predictive model for diagnosis of risk of thoracotomy in thoracoscopic lung cancer resection, when the computer program product is executed on an electronic device.

It is another object of the present invention to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to apply the system for constructing a prediction model for open chest risk diagnosis in thoracoscopic lung cancer resection.

By combining all the technical schemes, the invention has the advantages and positive effects that: the construction system of the open-chest risk diagnosis and prediction model in thoracoscopic lung cancer resection provided by the invention is used for establishing the open-chest risk diagnosis and prediction model in the lung cancer thoracoscopic surgery based on the ten thousands of data in the thoracic surgery of the western China hospital and the machine learning algorithms such as classical logistic regression, artificial neural network (artificial neural network) and random forest (random forest), and can realize online prediction of the open-chest risk in the thoracoscopic lung cancer resection after inputting some indexes based on the established webpage version nomogram (https:// ict-wch.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a block diagram of a system for constructing a prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection according to an embodiment of the present invention;

in the figure: 1. a data acquisition module; 2. a risk diagnosis model construction module; 3. a data preprocessing module; 4. a data fusion module; 5. a data set training module; 6. a model verification and optimization module; 7. a central control module; 8. a risk diagnosis prediction module; 9. a data storage module; 10. and updating the display module.

Fig. 2 is a flowchart of a method for constructing a prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection according to the embodiment of the present invention.

Fig. 3 is a flowchart of a method for obtaining a risk prediction index according to an embodiment of the present invention.

Fig. 4 is a flowchart of a method for inputting a sample set into the risk diagnosis prediction model for training and learning by a data set training module using a data set training program according to an embodiment of the present invention.

Fig. 5 is a flowchart of a method for diagnosing and predicting open chest risk in thoracoscopic lung cancer resection of a clinical patient by using a chest open risk diagnosis prediction model in thoracoscopic lung cancer resection and calculating a comprehensive risk value through a risk diagnosis prediction module according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Aiming at the problems in the prior art, the invention provides a prediction model and a construction system for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection, and the invention is described in detail with reference to the accompanying drawings.

As shown in fig. 1, a system for constructing a prediction model for diagnosing risk of thoracotomy in thoracoscopic lung cancer resection provided by the embodiment of the present invention includes: the risk diagnosis system comprises a data acquisition module 1, a risk diagnosis model construction module 2, a data preprocessing module 3, a data fusion module 4, a data set training module 5, a model verification and optimization module 6, a central control module 7, a risk diagnosis prediction module 8, a data storage module 9 and an update display module 10.

The data acquisition module 1 is connected with the central control module 7 and used for acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

the risk diagnosis model building module 2 is connected with the central control module 7 and used for building a thoracotomy risk diagnosis prediction model in thoracoscopic lung cancer resection through a model building program based on a machine learning algorithm including classical logistic regression, an artificial neural network and a random forest;

the data preprocessing module 3 is connected with the central control module 7 and is used for carrying out noise reduction and normalization processing on the acquired data through a data preprocessing program and marking and dividing an interested area corresponding to a pathological result on an image;

the data fusion module 4 is connected with the central control module 7 and used for carrying out fusion processing on various types of preprocessed data information of the clinical patient through a data fusion program to obtain a risk diagnosis prediction data set and dividing the data set into a sample set and a test set;

the data set training module 5 is connected with the central control module 7 and is used for inputting the sample set into the risk diagnosis prediction model for training and learning through a data set training program and inputting the test set into the risk diagnosis prediction model for testing after training;

the model verification and optimization module 6 is connected with the central control module 7 and is used for evaluating the effectiveness, the generalization and the robustness of the model under different parameters and scenes and performing iterative optimization on the performance of the model by adopting a reward and punishment mechanism;

the central control module 7 is connected with the data acquisition module 1, the risk diagnosis model construction module 2, the data preprocessing module 3, the data fusion module 4, the data set training module 5, the model verification and optimization module 6, the risk diagnosis prediction module 8, the data storage module 9 and the update display module 10, and is used for cooperatively controlling the normal operation of each module of the construction system of the thoracoscopic risk diagnosis prediction model in thoracoscopic lung cancer resection through a central processing unit and/or a single chip microcomputer;

the risk diagnosis and prediction module 8 is connected with the central control module 7 and is used for diagnosing and predicting the chest opening risk in the thoracoscopic lung cancer resection of the clinical patient through the chest opening risk diagnosis and prediction model in the thoracoscopic lung cancer resection and calculating the comprehensive risk value;

the data storage module 9 is connected with the central control module 7 and used for storing various acquired data information of clinical patients, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the risk diagnosis prediction result through a memory;

and the updating display module 10 is connected with the central control module 7 and is used for updating and displaying various acquired data information of the clinical patient, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the real-time data of the risk diagnosis prediction result through a display.

As shown in fig. 2, the method for constructing the open chest risk diagnosis prediction model in thoracoscopic lung cancer resection provided by the embodiment of the invention comprises the following steps:

s101, acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians by using data acquisition equipment through a data acquisition module;

s102, constructing a chest opening risk diagnosis prediction model in thoracoscopic lung cancer resection by using a risk diagnosis model construction module and a model construction program based on a machine learning algorithm including classical logistic regression, an artificial neural network and a random forest;

s103, carrying out noise reduction and normalization processing on the acquired data by using a data preprocessing program through a data preprocessing module, and marking and dividing an interested area corresponding to a pathological result on an image;

s104, performing fusion processing on various types of preprocessed clinical patient data information by using a data fusion program through a data fusion module to obtain a risk diagnosis prediction data set, and dividing the data set into a sample set and a test set;

s105, inputting a sample set into the risk diagnosis prediction model for training and learning by a data set training module through a data set training program, and inputting a test set into the risk diagnosis prediction model for testing after training;

s106, evaluating the effectiveness, the generalization and the robustness of the model under different parameters and scenes through a model verification and optimization module, and performing iterative optimization on the performance of the model by adopting a reward and punishment mechanism; the normal operation of each module of the construction system of the open chest risk diagnosis and prediction model in the thoracoscopic lung cancer resection is coordinately controlled by a central control module through a central processor and/or a single chip microcomputer;

s107, diagnosing and predicting the thoracotomy transit risk diagnosis and prediction model of the thoracoscopic lung cancer resection of the clinical patient by using the risk diagnosis and prediction module, and calculating a risk comprehensive probability value;

s108, the data storage module is used for storing various acquired data information of the clinical patient, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the risk diagnosis prediction result by using the memory;

and S109, updating and displaying the acquired various data information of the clinical patient, the constructed risk diagnosis prediction model, the data preprocessing result, the risk diagnosis prediction data set, the data set training result, the model verification and optimization result and the real-time data of the risk diagnosis prediction result by using the display through the updating and displaying module.

As shown in fig. 3, the obtaining of the risk prediction index according to the embodiment of the present invention includes:

s201, acquiring a plurality of clinical indexes of clinical patients through professional medical equipment;

s202, after the integrity and the quality of the data of the plurality of clinical indexes are verified and evaluated, desensitization processing is carried out on the data, and privacy information and medical institution information of a patient are hidden;

s203, taking a plurality of clinical indexes with clinical evaluation indexes higher than a first threshold value as prediction indexes of the risk of thoracotomy in thoracoscopic lung cancer resection of the clinical patient.

In the data preprocessing module provided by the embodiment of the invention, a doctor searches for a lesion corresponding to a pathological result, and then performs contour segmentation and local block segmentation on the lesion by using an automatic segmentation method modified and confirmed by the doctor or a manual segmentation method adopted by the doctor.

As shown in fig. 4, the inputting of the sample set into the risk diagnosis prediction model for training and learning through the data set training module by using the data set training program according to the embodiment of the present invention includes:

s301, acquiring training data and training the risk diagnosis prediction model according to the training data to obtain a first training model;

s302, obtaining a second training model according to partial parameters of the first training model;

s303, training the second training model according to the loss function between the first training model and the second training model to obtain a risk diagnosis prediction model.

The step of training the risk diagnosis prediction model according to the training data to obtain a first training model provided by the embodiment of the invention comprises the following steps:

preprocessing training data to obtain a text sequence and label data corresponding to the training data;

performing probability calculation processing on the text sequence and the label data according to a preset model to obtain a probability value, wherein the probability value represents the corresponding relation between a word vector corresponding to the text sequence and the label data;

and training the risk diagnosis prediction model according to the probability value to obtain a first training model.

As shown in fig. 5, the risk diagnosis and prediction module according to the embodiment of the present invention utilizes a thoracotomy transit risk diagnosis and prediction model in thoracoscopic lung cancer resection to diagnose and predict thoracotomy transit risk in thoracoscopic lung cancer resection of a clinical patient, and calculates a risk comprehensive probability value, including:

s401, acquiring body characteristic data, image information, risk prediction indexes, gene information, pathological benign and malignant labels of clinical patients and diagnosis result data given by clinicians through data acquisition equipment;

s402, extracting index data based on the acquired image data; after the acquired image information is processed, whether a tumor exists in the image is judged by analyzing the processed image information, and if the tumor exists, whether the size of the tumor exceeds a preset threshold value is judged; meanwhile, judging whether the lymph nodes in the image have tumors, whether the tumors are immersed in blood vessels and the degree of adhesion of the thoracic cavity, and obtaining an analysis judgment result of the image;

s403, calculating and generating corresponding clinical indexes based on the extracted index data, the obtained body characteristic data of the clinical patient, the image information, the risk prediction indexes, the gene information, the pathological benign and malignant label, the diagnosis result data given by a clinician and the analysis and judgment result of the image, and inputting the clinical indexes into a risk diagnosis prediction model for risk prediction;

s404, the risk diagnosis prediction model outputs risk probability corresponding to each clinical index according to each input clinical index, and calculates the operation risk comprehensive probability value of the clinical patient.

The extraction of the index data based on the acquired image data provided by the embodiment of the invention comprises the following steps:

performing character region filtering processing on the medical image digital image to obtain a binary image after character region filtering; removing isolated points from the binary image to obtain an updated binary image;

scanning the obtained updated binary image in the horizontal direction and the vertical direction to obtain the size of each binary medical image and position information in the binary image;

dividing each binary medical image into four equal parts according to the form of width and height half, and respectively performing line scanning and column scanning on each equal part to respectively obtain the height and width of a character area in the equal part; obtaining the size and position information of all character areas of four equal parts in each binary medical image;

and extracting the character areas of four equal parts in each binary medical image.

The embodiment of the invention provides a method for calculating the operation risk comprehensive probability value of a clinical patient by adopting the following formula:

wherein the parameter n represents the number of clinical indicators input to the risk diagnosis prediction model，R_iRepresents the operation risk probability predicted by a clinical index through a risk diagnosis prediction model, W_iRepresents a weighted impact factor of the ith clinical indicator on surgical risk.

In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.