Medical examination consultation system based on medical big data and use method

Abstract

The invention relates to a medical examination consultation system based on medical big data and a use method, and belongs to the technical field of medical examination consultation. Comprising a patient information input module, an information processing module, a medical big data analysis platform, a desensitization information processing module, a medical big data machine learning module, an inspection result data warehouse and a consultation result output module. The system is characterized in that patient data of the patient information input module comprises patient age and gender information, patient case historical data, patient examination data, patient image examination data, patient diagnosis results and treatment schemes. According to the system, medical big data learning can be carried out on existing case data, so that a corresponding diagnosis database can be formed, machine learning can be carried out on similar diseases, a data warehouse can be formed, when diagnosis auxiliary consultation is needed, patient information can be input into a big data analysis module, the data warehouse can be compared according to the patient information, and the diagnosis auxiliary consultation efficiency is improved. And outputting a consultation judgment result.

Description

Technical Field

[0001] The present invention relates to the technical field of medical test consultation, and particularly to a medical test consultation system and a usage method based on medical big data. Background Art

[0002] Medical test, also known as clinical test or medical laboratory science, is a discipline that obtains information about an individual's health status by laboratory testing samples taken from the human body, such as blood, urine, tissue samples, etc., so as to provide important basis for disease prevention, diagnosis, treatment and post-treatment rehabilitation. Medical test plays an important role in disease diagnosis and treatment. Medical test consultation refers to a series of consultation services provided during the medical test process, aiming to help doctors, patients or other healthcare providers correctly understand and interpret medical test results, so as to make more accurate diagnosis and treatment decisions.

[0003] In traditional medical diagnosis behavior, doctors diagnose patients based on the patient's diagnostic indicators combined with knowledge reserves and experience. However, such a diagnosis mode has certain limitations, and the diagnostic accuracy depends on the doctor's subjective judgment. To assist doctors in diagnosis, a medical test consultation system and a usage method based on medical big data are proposed. It can perform medical big data learning on existing case data, thereby constituting a corresponding diagnostic database, and can perform machine learning on similar diseases to form a data warehouse. When diagnostic assistance consultation is needed, patient information can be input into the big data analysis module, and then the data warehouse can be compared according to the patient information, so as to output a consultation judgment result, and the judgment probability can be prompted according to the proportion of the judgment result in previous similar cases, and the corresponding similar case information can be extracted for doctors' reference, so as to effectively assist doctors in making diagnostic decisions, and the diagnostic results can be collected to update the medical big data analysis platform in real time. Summary of the Invention

[0004] The present invention provides a medical test consultation system and a usage method based on medical big data, which solve the problems proposed in the above-mentioned background art. By means of big data analysis technology, a consultation system for disease diagnosis is established through machine learning, thereby improving the accuracy of patient diagnosis and serving as an important tool for clinical auxiliary decision-making.

[0005] The solution of the present invention to the above technical problems is as follows: A medical test consultation system based on medical big data and its usage method, including a patient information input module, an information processing module, a medical big data analysis platform, a desensitized information processing module, a medical big data machine learning module, a test result data warehouse, and a consultation result output module. The patient data of the patient information input module includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans. The information processing module includes a data cleaning module, a data desensitization module, a data splitting and classification module, and an error data processing module.

[0006] The usage method includes the following steps:

[0007] S1: The medical record information of each hospital is subjected to information desensitization processing through the desensitized information processing module to form a training case set. The training case set is input into the medical big data machine learning module. The medical big data machine learning module performs machine learning on the medical records of the hospital to form a test result data warehouse, thereby statistically analyzing and classifying the diagnosis results corresponding to the hospital test results. The data in the test result data warehouse is imported into the medical big data analysis platform as reference data;

[0008] S2: Input patient information in the patient information data input module. The information processing module performs data cleaning, data desensitization, data splitting and classification, and error data processing on the patient age and gender information, patient case history data, patient test data, and patient imaging test data. The processed patient information data is imported into the medical big data analysis platform;

[0009] S3: The medical big data analysis platform exports the consultation result through the consultation result output module according to the data in the test result data warehouse, and the doctor can make a decision under the action of the consultation result;

[0010] S4: If the consultation result can assist the doctor in completing the diagnosis decision and treatment, the diagnosis decision is used as the diagnosis result data and imported into the training case set through the information desensitization processing module for machine learning again. If the consultation result cannot assist the doctor in completing the diagnosis decision and treatment, additional test items are added.

[0011] Based on the above technical solutions, the present invention can also be improved as follows.

[0012] Further, in S3, the consultation result includes a disease determination result, a determination probability, and corresponding similar case information.

[0013] Further, in S3, the top three consultation results are selected according to the determination probability for assisting the doctor in making a decision, and other possible disease results are listed.

[0014] Furthermore, the hospital visit case information includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

[0015] Furthermore, the diagnosis result data includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

[0016] The beneficial effects of the present invention are as follows: The present invention provides a medical test consultation system and usage method based on medical big data, having the following advantages:

[0017] 1. It can perform medical big data learning on existing case data, thereby constituting a corresponding diagnosis database, and can perform machine learning on similar diseases to form a data warehouse. When diagnostic assistance consultation is required, patient information can be input into the big data analysis module, and then the patient information can be compared with the data warehouse to output a consultation judgment result, and the judgment probability can be prompted according to the proportion of the judgment result in previous similar cases, and the corresponding similar case information can be extracted for doctors' reference, thus effectively assisting doctors in making diagnostic decisions;

[0018] 2. It can collect the diagnosis results of the hospital in real time, thereby updating the medical big data analysis platform in real time, facilitating the recording and statistics of epidemics and infectious diseases, and enabling doctors to quickly diagnose and propose treatment plans during the peak periods when epidemics and infectious diseases frequently occur, improving the diagnostic efficiency.

[0019] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it in accordance with the content of the description, the following takes the preferred embodiments of the present invention and combines with the accompanying drawings to describe in detail as follows. The specific implementation manners of the present invention are given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and the illustrative embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0021] Figure 1 is the system flowchart of a medical test consultation system and usage method based on medical big data provided by an embodiment of the present invention;

[0022] Figure 2 is the schematic diagram of hospital visit case information in a medical test consultation system and usage method based on medical big data provided by an embodiment of the present invention;

[0023] Figure 3Schematic diagram of patient information data in a medical test consultation system and its usage method based on medical big data provided by an embodiment of the present invention;

[0024] Figure 4 Schematic diagram of diagnostic result data in a medical test consultation system and its usage method based on medical big data provided by an embodiment of the present invention;

[0025] Figure 5 Schematic diagram of an information processing module in a medical test consultation system and its usage method based on medical big data provided by an embodiment of the present invention. Detailed implementation manners

[0026] The principles and features of the present invention will be described below in conjunction with the attached Figures 1-5 The principles and features of the present invention will be described below, and the examples given are only used to explain the present invention and are not intended to limit the scope of the present invention. The present invention will be described more specifically by way of example in the following paragraphs with reference to the accompanying drawings. The advantages and features of the present invention will be clearer according to the following description and the claims. It should be noted that the attached drawings are all in a very simplified form and use non-precise scales, and are only used to facilitate and clearly assist in explaining the purpose of the embodiments of the present invention.

[0027] It should be noted that when a component is referred to as "fixed to" another component, it can be directly on the other component or there may also be an intermediate component. When a component is considered to be "connected" to another component, it can be directly connected to the other component or there may be an intermediate component at the same time. When a component is considered to be "disposed on" another component, it can be directly disposed on the other component or there may be an intermediate component at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0029] Such as Figures 1-5As shown in the figure, the present invention provides a medical test consultation system and its usage method based on medical big data, including a patient information input module, an information processing module, a medical big data analysis platform, a desensitized information processing module, a medical big data machine learning module, a test result data warehouse, and a consultation result output module. The patient data of the patient information input module includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

[0030] Preferably, the hospital visit case information includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

[0031] Preferably, the diagnosis result data includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

[0032] The specific working principle and usage method of the present invention are as follows:

[0033] S1: The visit case information of each hospital is subjected to information desensitization processing through the desensitized information processing module to form a training case set. The training case set is input into the medical big data machine learning module. The medical big data machine learning module conducts machine learning on the hospital visit cases to form a test result data warehouse, thereby statistically analyzing and classifying the diagnosis results corresponding to the hospital test results. The data in the test result data warehouse is imported into the medical big data analysis platform as reference data;

[0034] S2: Input patient information in the patient information data input module. Through the information processing module, data cleaning, data desensitization, data splitting and classification, and error data processing are performed on the patient age and gender information, patient case history data, patient test data, and patient imaging test data. The processed patient information data is imported into the medical big data analysis platform;

[0035] S3: The medical big data analysis platform exports the consultation results through the consultation result output module based on the data in the test result data warehouse. Doctors can make decisions under the influence of the consultation results. The consultation results include disease determination results, determination probabilities, and corresponding similar case information. The top three consultation results are selected according to the determination probability to assist doctors in making decisions, and other possible disease results are listed;

[0036] S4: If the consultation result can assist the doctor in making a diagnosis decision and completing the treatment, the diagnosis decision is imported into the training case set through the information desensitization processing module as the diagnosis result data for machine learning again. If the consultation result cannot assist the doctor in making a diagnosis decision and completing the treatment, additional test items are added.

[0037] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0038] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention; any ordinary technician in the industry can smoothly implement the present invention according to the description shown in the accompanying drawings of the specification and the above description; however, any equivalent changes made by those skilled in the art within the scope of the technical solution of the present invention by using the technical content disclosed above for minor modifications, decorations, and evolutions are equivalent embodiments of the present invention; at the same time, any equivalent changes, modifications, and evolutions made to the above embodiments based on the essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A medical test consultation system based on medical big data, comprising a patient information input module, an information processing module, a medical big data analysis platform, a desensitized information processing module, a medical big data machine learning module, a test result data warehouse, and a consultation result output module. Characterized in that: The patient data of the patient information input module includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans. The information processing module includes a data cleaning module, a data desensitization module, a data splitting and classification module, and an error data processing module. The usage method includes the following steps: S1: The medical record information of each hospital is desensitized by the desensitized information processing module to form a training case set. The training case set is input into the medical big data machine learning module, and the medical big data machine learning module performs machine learning on the hospital medical records to form a test result data warehouse, so as to statistically classify the diagnosis results corresponding to the hospital test results. The data in the test result data warehouse is imported into the medical big data analysis platform as reference data. S2: Input patient information in the patient information data input module. The information processing module performs data cleaning, data desensitization, data splitting and classification, and error data processing on the patient age and gender information, patient case history data, patient test data, and patient imaging test data. The processed patient information data is imported into the medical big data analysis platform. S3: The medical big data analysis platform exports the consultation result through the consultation result output module according to the data in the test result data warehouse, and the doctor can make a decision under the action of the consultation result. S4: If the consultation result can assist the doctor in completing the diagnosis decision and treatment, the diagnosis decision is imported into the training case set through the information desensitization processing module for machine learning again. If the consultation result cannot assist the doctor in completing the diagnosis decision and treatment, additional test items are added.

2. The medical test consultation system based on medical big data according to claim 1. Characterized in that, In S3, the consultation result includes a disease determination result, a determination probability, and corresponding similar case information.

3. The medical test consultation system based on medical big data according to claim 1. Characterized in that, In S3, the top three consultation results are selected according to the determination probability for assisting the doctor in making a decision, and other possible disease results are listed.

4. The medical test consultation system based on medical big data according to claim 1. Characterized in that, The hospital medical record information includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

5. The medical test consultation system based on medical big data according to claim 1. Characterized in that, The diagnosis result data includes patient age and gender information, patient case history data, patient test data, patient imaging test data, patient diagnosis results, and treatment plans.

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