CN115223707A - Medical artificial intelligence application evaluation system based on doctor feedback - Google Patents
Medical artificial intelligence application evaluation system based on doctor feedback Download PDFInfo
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- CN115223707A CN115223707A CN202210803921.6A CN202210803921A CN115223707A CN 115223707 A CN115223707 A CN 115223707A CN 202210803921 A CN202210803921 A CN 202210803921A CN 115223707 A CN115223707 A CN 115223707A
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention relates to the technical field of medical artificial intelligence, and discloses a medical artificial intelligence application evaluation system based on doctor feedback, which comprises a medical artificial intelligence platform and a medical big database, wherein the medical artificial intelligence platform is in signal connection with an automatic question-answering module and a doctor docking module, the medical big database is used for carrying out original input of cleaning, structuring and integration on the existing medical data, the medical artificial intelligence platform can analyze and utilize the data in the medical big database, then the problems are solved through machine learning and interpretation according to the data, the symptoms of a patient are uploaded to the medical artificial intelligence platform through a patient question-diagnosing module and the automatic question-answering module, meanwhile, the patient question-diagnosing module submits a patient data module comprising a patient family medical history, a patient past medical history and a patient examination report together, and the patient symptom analysis module can analyze the patient information obtained from the automatic question-answering module and obtain the symptoms of the patient through an empirical analysis library and a follow-up analysis library.
Description
Technical Field
The invention relates to the technical field of medical artificial intelligence, in particular to a medical artificial intelligence application evaluation system based on doctor feedback.
Background
The main problems of the current medical and health work in China are that medical resources are not distributed evenly, general doctors needed by primary medical institutions are lacked, the culture period is long, and talents are difficult to keep; the patient satisfaction is low, but with the rapid development of artificial intelligence, the Internet and big data, people can utilize a large amount of currently mastered medical data by establishing a medical artificial intelligence platform and through the data analysis and the machine learning of the artificial intelligence, and can realize that a computer assists the brain to carry out diagnosis and decision.
However, because of the limitations of the artificial intelligence, an error may occur in the diagnosis decision made by the artificial intelligence, and therefore, the diagnosis decision made by the artificial intelligence needs to be checked, and the diagnosis decision is continuously corrected, so that the diagnosis error rate is continuously reduced in each machine learning.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a medical artificial intelligence application evaluation system based on doctor feedback, which solves the problems in the background technology.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a medical artificial intelligence application evaluation system based on doctor feedback, includes medical artificial intelligence platform and medical big database, medical artificial intelligence platform signal connection has automatic question-answering module and doctor to dock the module, medical big database is used for clearing up, structurize and integrated primitive input with current medical data, medical artificial intelligence platform can the ware analysis and utilize data in the medical big database, then solve the problem through machine learning explanation according to these data to patient's question-answering data that obtains from automatic question-answering module is reacted, takes corresponding action, the problem that the automatic question-answering module will be proposed to the patient is submitted medical artificial intelligence platform, transmits the result that medical artificial intelligence platform passed down to the patient, doctor to dock the module and be used for carrying out information transfer and communication between medical artificial intelligence platform and doctor and the doctor.
Preferably, the automatic question-answering module is in signal connection with a patient inquiry module, the patient inquiry module is in signal connection with a patient data module and a later-period curative effect module, the patient data module comprises a patient family medical history, a patient past medical history and a patient physical examination report, and the later-period curative effect module comprises a patient rehabilitation state and a patient abnormal response.
Preferably, the medical big database is in signal connection with a patient symptom analysis module, the patient symptom analysis module is in signal connection with an empirical analysis library and a evidence-based analysis library, the empirical analysis library and the evidence-based analysis library obtain inquiry results through a meta analysis system, the patient symptom analysis module can analyze patient information obtained from the automatic question and answer module and obtain symptoms of the patient through the empirical analysis library and the evidence-based analysis library, the evidence-based analysis library is derived through differentiation and induction of a large amount of clinical data in the medical big database, and the empirical analysis library is compared and judged from previous patient information with similar symptoms.
Preferably, the meta analysis system can comprehensively collect all relevant cases from the medical big database, analyze the cases one by one, screen out available cases, evaluate reference values of the cases, analyze main quantitative indexes in the cases through meta analysis by a quantitative synthesis method to obtain a uniform result, and finally perform targeted optimization on the obtained result to obtain an inquiry result and feed the inquiry result back to the medical artificial intelligence platform.
Preferably, the artificial intelligence platform is capable of uploading the obtained inquiry result to the doctor docking module, the doctor docking module is in signal connection with a discussion group module, the discussion group module is capable of completing remote information connection of several or more doctors, the discussion group module is in signal connection with a doctor discussion module, and the doctor discussion module is capable of enabling doctors to conduct real-time communication discussion, including discussing the patient symptoms obtained by the patient symptom analysis module and the inquiry result obtained by the meta analysis system, judging the accuracy of the patient symptoms, and determining whether to adopt the inquiry result obtained by the meta analysis system and determining a final diagnosis scheme by the doctors participating in the discussion.
Preferably, the final diagnosis protocol is uploaded to the medical artificial intelligence platform, and the medical artificial intelligence platform then transmits the final diagnosis protocol to the patient through the automatic question-answering module.
Preferably, the medical artificial intelligence platform analyzes errors and reasons generated by the inquiry result and the final diagnosis scheme by comparing the inquiry result with the final diagnosis scheme, feeds back and machine-learns the generated errors, and corrects the empirical analysis library and the evidence-based analysis library at the same time, so as to improve the accuracy of the next inquiry result.
(III) advantageous effects
The invention provides a medical artificial intelligence application evaluation system based on doctor feedback, which has the following beneficial effects:
(1) The patient condition analysis module can analyze the patient information obtained from the automatic question-answering module and obtain the disease symptoms of the patient through an experience analysis library and a evidence-based analysis library.
(2) The invention can comprehensively collect all relevant cases from a medical big database through a meta analysis system, analyze the cases one by one, screen out available cases, evaluate the reference value of the cases, analyze the main quantitative indexes through meta analysis to obtain a uniform result, and finally perform targeted optimization on the obtained result to determine the cause of disease and finally obtain an inquiry result.
(3) The doctor docking module and the discussion group module enable several or more doctors to communicate and discuss in real time, judge the accuracy of the inquiry result obtained by the meta analysis system and determine the final diagnosis scheme, meanwhile, the medical artificial intelligence platform analyzes the error and the reason generated by the inquiry result and the final diagnosis scheme by comparing the inquiry result with the final diagnosis scheme, feeds back the generated error and learns by a machine, corrects the empirical analysis library and the evidence-based analysis library so as to improve the accuracy of the next inquiry result, and continuously reduces the diagnosis error rate in each machine learning by continuously correcting the diagnosis decision, so that the artificial intelligence is more practical in diagnosis.
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FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic view of meta analysis system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-2, the present invention provides a technical solution: a medical artificial intelligence application evaluation system based on doctor feedback comprises a medical artificial intelligence platform and a medical big database, wherein the medical artificial intelligence platform is in signal connection with an automatic question-answer module and a doctor docking module, the medical big database is used for carrying out original input of cleaning, structuring and integrating existing medical data, the medical artificial intelligence platform can analyze and utilize the data in the medical big database and then solve problems through machine learning and interpretation according to the data, so as to react on the patient question-answer data obtained from the automatic question-answer module and take corresponding actions, the automatic question-answer module provides the problems proposed for patients to the medical artificial intelligence platform and transmits the results transmitted by the medical artificial intelligence platform to the patients, the doctor docking module is used for transmitting and communicating information between the medical artificial intelligence platform and doctors and between the doctors, the automatic question-answer module is in signal connection with a patient data module and a later curative effect module, the patient data module comprises a family history of the patients, the past medical history of the patients and patient reports, the curative effect module comprises a patient state and a later stage response, the patient data module is in signal connection with a patient data base, the patient data base is connected with a patient data analysis module and a clinical analysis module, and a patient analysis module is connected with a clinical analysis module to derive a patient analysis and a patient analysis module to carry out symptom analysis and a clinical analysis and a patient analysis module to carry out a patient analysis and a patient analysis by a patient analysis module, the experience analysis library is used for comparing and judging the previous patient information with similar symptoms, the meta analysis system can comprehensively collect all cases related to related symptoms from a medical big database, analyze the cases one by one, screen out available cases and evaluate the reference value of the cases, the main quantitative indexes are analyzed by the meta analysis, a unified result is obtained by quantitative synthesis, the obtained result is optimized in a targeted mode finally, an inquiry result is obtained and fed back to a medical artificial intelligence platform, the artificial intelligence platform can upload the obtained inquiry result to a doctor docking module, the doctor docking module is in signal connection with a discussion group module, the discussion group module can complete remote information connection of several or more doctors, the discussion group module is in signal connection with a doctor discussion module, the doctor discussion module can enable the doctors to conduct real-time communication discussion, the method comprises the steps of discussing the patient symptoms obtained by the patient symptom analysis module and the inquiry result obtained by the meta analysis system, judging the accuracy of the patient symptoms and the final diagnosis result obtained by the meta analysis system, determining whether the patient symptoms obtained by the patient symptom analysis system and determining the final diagnosis result, and automatically transmitting the results obtained by the manual diagnosis platform, and automatically correcting the results obtained by the manual diagnosis platform, and the manual diagnosis result analysis and the manual diagnosis result obtained by the manual diagnosis platform, and the manual diagnosis result analysis platform, and the final diagnosis error correction method can generate the error.
In conclusion, the work flow of the invention is as follows: when the patient diagnosis and treatment system is used specifically, a patient uploads symptoms of the patient to a medical artificial intelligence platform through a patient inquiry module and an automatic inquiry and answer module, the patient inquiry module submits a patient data module including family medical history, past medical history and patient physical examination reports of the patient together, the patient symptom analysis module can analyze the patient information obtained from the automatic inquiry and answer module and obtain symptoms of the patient through an empirical analysis library and a syndrome-based analysis library, the syndrome-based analysis library is deduced by carrying out analysis and induction through a large amount of clinical data in a medical big database, the empirical analysis library is used for carrying out comparison and judgment on the previous patient information with similar symptoms, all relevant cases of relevant symptoms can be comprehensively collected from the medical big database through a meta analysis system, the cases can be analyzed one by one, the available cases can be screened out, and the reference value of the cases can be evaluated, the main quantitative indexes are analyzed through meta analysis, then a uniform result is obtained through quantitative synthesis, finally the obtained result is subjected to targeted optimization, the etiology is determined, an inquiry result is finally obtained, the obtained inquiry result enables several or more doctors to conduct real-time communication discussion through a doctor docking module and a discussion group module, the accuracy of the inquiry result obtained by a meta analysis system is judged, a final diagnosis scheme is determined, meanwhile, a medical artificial intelligence platform analyzes errors and reasons generated by the inquiry result and the final diagnosis scheme through comparison, the errors are fed back and machine learning is conducted, an experience analysis library and a evidence-based analysis library are corrected, the accuracy of the next inquiry result is improved, the diagnosis decision is continuously corrected, and the diagnosis error rate is continuously reduced in each machine learning, the artificial intelligence is more practical when diagnosis is carried out.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The utility model provides a medical artificial intelligence application evaluation system based on doctor feedback, includes medical artificial intelligence platform and medical big database, its characterized in that: the medical artificial intelligence platform is in signal connection with an automatic question-answer module and a doctor docking module, the medical big database is used for carrying out original input of cleaning, structuring and integration on existing medical data, the medical artificial intelligence platform can analyze and utilize data in the medical big database, and then solves problems through machine learning interpretation according to the data, so that the questions asked and asked for the patient obtained from the automatic question-answer module are responded, corresponding actions are taken, the problems asked for the patient are submitted to the medical artificial intelligence platform through the automatic question-answer module, the results downloaded by the medical artificial intelligence platform are transmitted to the patient, and the doctor docking module is used for carrying out information transmission and communication between the medical artificial intelligence platform and the doctor as well as between the doctor and the doctor.
2. The medical artificial intelligence application evaluation system based on doctor feedback according to claim 1, characterized in that: the automatic question-answering module is in signal connection with a patient inquiry module, the patient inquiry module is in signal connection with a patient data module and a later-stage curative effect module, the patient data module comprises a patient family medical history, a patient past medical history and a patient physical examination report, and the later-stage curative effect module comprises a patient rehabilitation state and a patient abnormal response.
3. The medical artificial intelligence application evaluation system based on doctor feedback according to claim 1, characterized in that: the medical big database is in signal connection with a patient symptom analysis module, the patient symptom analysis module is in signal connection with an empirical analysis base and a syndrome analysis base, the empirical analysis base and the syndrome analysis base obtain inquiry results through a meta analysis system, the patient symptom analysis module can analyze patient information obtained from the automatic inquiry and answer module and obtain symptoms through the empirical analysis base and the syndrome analysis base, the syndrome analysis base is obtained through analysis and induction of a large amount of clinical data in the medical big database, and the empirical analysis base is compared and judged from previous patient information with similar symptoms.
4. The medical artificial intelligence application evaluation system based on doctor feedback according to claim 3, characterized in that: the meta analysis system can comprehensively collect all relevant cases of relevant symptoms from the medical big database, analyze the cases one by one, screen out available cases, evaluate the reference value of the cases, analyze main quantitative indexes in the cases through meta analysis, analyze the main quantitative indexes by a quantitative synthesis method to obtain a uniform result, finally perform targeted optimization on the obtained result to obtain an inquiry result, and feed the inquiry result back to the medical artificial intelligence platform.
5. The medical artificial intelligence application evaluation system based on doctor feedback according to claim 1, characterized in that: the artificial intelligence platform can upload the obtained inquiry result to the doctor docking module, the doctor docking module is in signal connection with a discussion group module, the discussion group module can complete remote information connection of several or more doctors, the discussion group module is in signal connection with a doctor discussion module, and the doctor discussion module can enable doctors to conduct real-time communication discussion, including the steps of discussing the patient symptoms obtained through analysis by the patient symptom analysis module and the inquiry result obtained through the meta analysis system, judging the accuracy of the results, and determining whether to adopt the inquiry result obtained through the meta analysis system or not and determining a final diagnosis scheme by the doctors participating in discussion.
6. The medical artificial intelligence application evaluation system based on doctor feedback according to claim 5, characterized in that: and uploading the final diagnosis scheme to the medical artificial intelligence platform, and transmitting the final diagnosis scheme to a patient by the medical artificial intelligence platform through the automatic question-answering module.
7. The system of claim 5, wherein the system comprises: the medical artificial intelligence platform analyzes errors and reasons generated by the inquiry result and the final diagnosis scheme by comparing the inquiry result with the final diagnosis scheme, feeds back the generated errors and learns by a machine, and corrects the experience analysis library and the evidence-based analysis library at the same time so as to improve the accuracy of the inquiry result next time.
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| CN202210803921.6A CN115223707A (en) | 2022-07-07 | 2022-07-07 | Medical artificial intelligence application evaluation system based on doctor feedback |
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| CN202210803921.6A CN115223707A (en) | 2022-07-07 | 2022-07-07 | Medical artificial intelligence application evaluation system based on doctor feedback |
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