CN105678066A - Disease self-diagnosing method and system for completing data training based on user feedback information - Google Patents
Disease self-diagnosing method and system for completing data training based on user feedback information Download PDFInfo
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Abstract
The invention provides a disease self-diagnosing method for completing data training based on user feedback information. The method includes the following steps that a disease self-diagnosing data model is established and includes disease data, disease type data, symptom data and disease-symptom associated data; a user obtains a self-diagnosing result and a suggestion for treatment seeking through the disease self-diagnosing data model according to own symptoms; the user gives feedback according to the self-diagnosing result or the result of treatment seeking; the disease self-diagnosing data model performs analysis and learning according to the feedback to constantly perform data training. The method has the advantages that through the method, the disease self-diagnosing data model is established, the method for completing data training based on the user feedback information is established for providing a perfect data training path and method for disease self-diagnosing information, and more scientific and accurate bases are provided for self-diagnosing of the patient.
Description
Technical field
The invention belongs to computerized information field, especially relate to a kind of disease autodiagnosis method and system completing data training based on field feedback.
Background technology
Quickly, life stress is also very big, and this just brings a lot of secret worry for the healthy of people for the rhythm of life of present stage people. People once healthy go wrong, first-selected Shi Qu hospital, but the people seen a doctor in hospital seems again to be very many forever, he even some little symptom, the whole flow process seen a doctor is got off and can be spent a lot of time; And if people feel to waste time, be unwilling hospital, simply buys a little medicine according to the experience of oneself and takes, likely misses so again golden hour, delay the state of an illness.
Based on this phenomenon, if able to there is an information platform helping people to carry out disease autodiagnosis, people will be produced huge help, the sufferer of oneself by the content of information platform, in conjunction with the situation of self, first can be carried out the judgement at initial stage by people, symptom is slight, the simple treatment of oneself can be carried out according to the content of information platform, during the adventurous development trend of symptom, then go hospitalize.
Setting up such information platform helping people to carry out disease autodiagnosis, it is desirable to have a perfect data base about disease Yu feature, wherein data to pass through constantly training, the accuracy of guarantee autodiagnosis, provides quality services for people.
Summary of the invention
The problem to be solved in the present invention is a kind of disease autodiagnosis method completing data training based on field feedback of design, provides perfect data training approaches and methods for disease autodiagnosis information, and the autodiagnosis for sufferer provides science, accurately foundation.
It should be noted that, the present invention completes the disease autodiagnosis method of data training based on field feedback, it it is a kind of application of informatics, by the collection of relevant information, analysis so that provide more accurate take, more scientific diagnosis and treatment scheme, what not belong to disease diagnoses and treats method, does not therefore violate the relevant regulations of Patent Law Article 25.
In order to achieve the above object, the technical scheme that the present invention takes is: a kind of disease autodiagnosis method completing data training based on field feedback, it is characterised in that comprise the steps:
(1) disease autodiagnosis data model is set up, including disease data, disease type data, symptom data, disease-state associated data;
(2) user is according to self symptom, obtains autodiagnosis result by disease autodiagnosis data model and treatment is sought medical advice suggestion;
(3) user provides feedback according to autodiagnosis result or treatment result of seeking medical advice;
(4) disease autodiagnosis data model is analyzed according to feedback and learns, and constantly carries out data training.
Further, in the described disease autodiagnosis data model of step (1), described disease-state associated data includes the weight of disease and symptom, described disease data includes the symptom probability grand total of disease, and the symptom probability grand total of described disease is equal to the weight sum of this disease described in all symptom correspondences of this disease.
Further, the detailed process of described step (2) is:
(201) user selects common symptoms exhibited according to own situation;
(202) by disease autodiagnosis data model, diagnose according to the common symptoms exhibited that user selects;
(203) disease autodiagnosis data model exports diagnostic result to user and provides treatment and seek medical advice suggestion, it is recommended that section office of seeking medical advice.
Further, if user has characteristic symptom, after selecting common symptoms exhibited, reselection characteristic symptom.
Further, the symptom of confirmation, diagnostic result, Therapeutic Method when step (3) described feedback includes seeking medical advice.
Further, the method for the described data training of step (4) is:
(401) feedack being analyzed in screening step (3);
(402) the already present symptom data of disease autodiagnosis data model, it may be judged whether need to change original weight;
(403) emerging symptom data in user feedback, is added in the corresponding data of disease autodiagnosis data model, and configures preliminary weight;
(404) according to the weighted data updated, the symptom probability grand total obtaining disease is calculated.
A kind of should disease computer-aided diagnosis system in aforementioned manners, including autodiagnosis data base and autodiagnosis platform; Described autodiagnosis data base includes disease table, records all disease association data; Symptom type table, is used for recording symptom type; Symptom table, be used for record various symptom; Disease-state contingency table, it is used for recording related information between each disease and symptom; Described autodiagnosis platform association autodiagnosis data base, is provided with user's input module, diagnostic module, user feedback module, back-end data training module.
Further, described disease-state contingency table, field is set for recording the weight between certain disease and certain symptom; Being provided with the symptom probability grand total of field record disease in described disease data table, the symptom probability grand total of described disease is equal to the weight sum of this disease described in all symptom correspondences of this disease.
Further, described user's input module includes the input of user's common symptoms exhibited and the input of user's special module.
Further, described back-end data training module has been operated by backstage Medical Technologist.
The invention have the benefit that the method by the present invention, set up disease autodiagnosis data model, and set up a kind of method completing data training based on field feedback, there is provided perfect data training approaches and methods for disease autodiagnosis information, provide more scientific, foundation accurately for the autodiagnosis of sufferer.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the embodiment of the present invention.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention will be further described.
The method of the application present invention, sets up disease computer-aided diagnosis system, including autodiagnosis data base and autodiagnosis platform. Autodiagnosis data base include:
1) disease table (disease), records all disease association data.
2) symptom type table (symptom_type), is used for recording symptom type.
3) symptom table (symptom) is used for recording various symptom.
4) disease-state contingency table (disease_symptom), is used for the related information recording between each disease and symptom.
Disease table wherein has a field totalnum, is used for recording the symptom probability grand total of disease, is denoted as T;
Disease-state contingency table has a field realnum for recording the weight between certain disease and certain symptom, be denoted as R;
If there being disease A, the symptom associated with disease A has a, b, c, then can draw equation:
Namely the weight sum of A disease all symptoms corresponding A disease is equal with A disease probability grand total.
All symptoms of A disease association connection dynamically change according to user feedback situation for the weight of A disease.
The weight distribution initial value of each symptom is 1, and after being fed back, initial value adds 1 every time, and grand total T also adds 1 simultaneously. Then the probability of certain symptom is that this disease symptoms weight R is divided by grand total T. Illustrating, certain disease has 3 symptoms, then it is 3 that weight initial value is 1, T, and each disease symptoms probability is 33% (1/3); Certain symptom is by after member's feedback data, and value R becomes 2, then T becomes 4, and the probability of this disease symptoms becomes 50% (2/4), and the probability of other 2 symptoms all becomes 25% (1/4), by that analogy.
Described autodiagnosis platform association autodiagnosis data base, is provided with user's input module, diagnostic module, user feedback module, back-end data training module.
User uses the process of computer-aided diagnosis system as shown in Figure 1:
(1) after user's entrance system;
(2) relevant common symptoms exhibited is selected according to own situation;
(3) diagnosis is performed;
(4) if any characteristic symptom, then relevant characteristic symptom is selected further according to own situation;
(5) diagnosis is performed;
(6) output diagnostic result, and provide user's related advisory and recommend section office;
(7) user provides feedback according to diagnostic result;
(8) system according to feedback analysis and learns so that diagnostic function is more perfect.
The foregoing is only specific embodiments of the invention, the protection domain being not intended to limit the present invention, all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (10)
1. the disease autodiagnosis method completing data training based on field feedback, it is characterised in that comprise the steps:
(1) disease autodiagnosis data model is set up, including disease data, disease type data, symptom data, disease-state associated data;
(2) user is according to self symptom, obtains autodiagnosis result by disease autodiagnosis data model and treatment is sought medical advice suggestion;
(3) user provides feedback according to autodiagnosis result or treatment result of seeking medical advice;
(4) disease autodiagnosis data model is analyzed according to feedback and learns, and constantly carries out data training.
2. a kind of disease autodiagnosis method completing data training based on field feedback according to claim 1, it is characterized in that, in the described disease autodiagnosis data model of step (1), described disease-state associated data includes the weight of disease and symptom, described disease data includes the symptom probability grand total of disease, and the symptom probability grand total of described disease is equal to the weight sum of this disease described in all symptom correspondences of this disease.
3. a kind of disease autodiagnosis method completing data training based on field feedback according to claim 1 and 2, it is characterised in that the detailed process of described step (2) is:
(201) user selects common symptoms exhibited according to own situation;
(202) by disease autodiagnosis data model, diagnose according to the common symptoms exhibited that user selects;
(203) disease autodiagnosis data model exports diagnostic result to user and provides treatment and seek medical advice suggestion, it is recommended that section office of seeking medical advice.
4. a kind of disease autodiagnosis method completing data training based on field feedback according to claim 3, it is characterised in that if user has characteristic symptom, after selecting common symptoms exhibited, reselection characteristic symptom.
5. a kind of disease autodiagnosis method completing data training based on field feedback according to claim 1 and 2, it is characterised in that the symptom of confirmation, diagnostic result, Therapeutic Method when step (3) described feedback includes seeking medical advice.
6. a kind of disease autodiagnosis method completing data training based on field feedback according to claim 2, it is characterised in that the method for the described data training of step (4) is:
(401) feedack being analyzed in screening step (3);
(402) the already present symptom data of disease autodiagnosis data model, it may be judged whether need to change original weight;
(403) emerging symptom data in user feedback, is added in the corresponding data of disease autodiagnosis data model, and configures preliminary weight;
(404) according to the weighted data updated, the symptom probability grand total obtaining disease is calculated.
7. the disease computer-aided diagnosis system applying method described in claim 1, it is characterised in that including: autodiagnosis data base and autodiagnosis platform; Described autodiagnosis data base includes disease table, records all disease association data; Symptom type table, is used for recording symptom type; Symptom table, be used for record various symptom; Disease-state contingency table, it is used for recording related information between each disease and symptom; Described autodiagnosis platform association autodiagnosis data base, is provided with user's input module, diagnostic module, user feedback module, back-end data training module.
8. disease computer-aided diagnosis system according to claim 7, it is characterised in that described disease-state contingency table, arranges field for recording the weight between certain disease and certain symptom; Being provided with the symptom probability grand total of field record disease in described disease data table, the symptom probability grand total of described disease is equal to the weight sum of this disease described in all symptom correspondences of this disease.
9. disease computer-aided diagnosis system according to claim 7, it is characterised in that described user's input module includes the input of user's common symptoms exhibited and the input of user's special module.
10. disease computer-aided diagnosis system according to claim 7, it is characterised in that described back-end data training module has been operated by backstage Medical Technologist.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599574A (en) * | 2016-12-13 | 2017-04-26 | 天津迈沃医药技术股份有限公司 | Diagnosis and treatment data analysis method and system based on medical information platform |
| CN106777966A (en) * | 2016-12-13 | 2017-05-31 | 天津迈沃医药技术股份有限公司 | Data interactive training method and system based on medical information platform |
| CN107729710A (en) * | 2016-08-11 | 2018-02-23 | 宏达国际电子股份有限公司 | Media can be read in medical system, medical procedures and non-transient computer |
| CN108231137A (en) * | 2016-12-15 | 2018-06-29 | 童综合医疗社团法人童综合医院 | A kind of medical system with feedback study |
| CN109087691A (en) * | 2018-08-02 | 2018-12-25 | 科大智能机器人技术有限公司 | A kind of OTC drugs recommender system and recommended method based on deep learning |
| CN109326352A (en) * | 2018-10-26 | 2019-02-12 | 腾讯科技（深圳）有限公司 | Disease forecasting method, apparatus, terminal and storage medium |
| CN109616167A (en) * | 2018-12-12 | 2019-04-12 | 天津迈沃医药技术股份有限公司 | Doctor based on disease circle confuses method and system |
| CN109743363A (en) * | 2018-12-18 | 2019-05-10 | 广州圆爱康生物科技有限公司 | A kind of medical information intelligently pushing method and device |
| CN110379505A (en) * | 2019-06-10 | 2019-10-25 | 天津开心生活科技有限公司 | A kind of recognition methods, device, readable medium and the electronic equipment of the common processing mode of disease |
| CN111712186A (en) * | 2017-12-20 | 2020-09-25 | 医鲸股份有限公司 | Method and device for assisting in the diagnosis of cardiovascular diseases |
| CN111951955A (en) * | 2020-08-13 | 2020-11-17 | 神州数码医疗科技股份有限公司 | Method and device for constructing clinical decision support system based on rule reasoning |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1246942A (en) * | 1996-07-12 | 2000-03-08 | 第一咨询公司 | Computerized medical diagnostic system utilizing list-based processing |
| CN102521500A (en) * | 2011-12-06 | 2012-06-27 | 中国电子科技集团公司第十五研究所 | System and method for analyzing health data |
| US20130197884A1 (en) * | 2012-02-01 | 2013-08-01 | Siemens Corporation | Method and System for Advanced Measurements Computation and Therapy Planning from Medical Data and Images Using a Multi-Physics Fluid-Solid Heart Model |
| CN105069505A (en) * | 2015-08-14 | 2015-11-18 | 北京农信互联科技有限公司 | Swine disease knowledge database automatic training learning method and swine disease auxiliary diagnostic device |
-
2015
- 2015-12-31 CN CN201511033293.4A patent/CN105678066B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1246942A (en) * | 1996-07-12 | 2000-03-08 | 第一咨询公司 | Computerized medical diagnostic system utilizing list-based processing |
| CN102521500A (en) * | 2011-12-06 | 2012-06-27 | 中国电子科技集团公司第十五研究所 | System and method for analyzing health data |
| US20130197884A1 (en) * | 2012-02-01 | 2013-08-01 | Siemens Corporation | Method and System for Advanced Measurements Computation and Therapy Planning from Medical Data and Images Using a Multi-Physics Fluid-Solid Heart Model |
| CN105069505A (en) * | 2015-08-14 | 2015-11-18 | 北京农信互联科技有限公司 | Swine disease knowledge database automatic training learning method and swine disease auxiliary diagnostic device |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107729710A (en) * | 2016-08-11 | 2018-02-23 | 宏达国际电子股份有限公司 | Media can be read in medical system, medical procedures and non-transient computer |
| CN107729710B (en) * | 2016-08-11 | 2021-04-13 | 宏达国际电子股份有限公司 | Medical system and non-transitory computer readable medium |
| CN106599574B (en) * | 2016-12-13 | 2019-04-30 | 天津迈沃医药技术股份有限公司 | Diagnosis and treatment data analysing method and system based on medical information platform |
| CN106777966A (en) * | 2016-12-13 | 2017-05-31 | 天津迈沃医药技术股份有限公司 | Data interactive training method and system based on medical information platform |
| CN106777966B (en) * | 2016-12-13 | 2020-02-07 | 天津迈沃医药技术股份有限公司 | Data interactive training method and system based on medical information platform |
| CN106599574A (en) * | 2016-12-13 | 2017-04-26 | 天津迈沃医药技术股份有限公司 | Diagnosis and treatment data analysis method and system based on medical information platform |
| CN108231137A (en) * | 2016-12-15 | 2018-06-29 | 童综合医疗社团法人童综合医院 | A kind of medical system with feedback study |
| US11869184B2 (en) | 2017-12-20 | 2024-01-09 | Medi Whale Inc. | Method and device for assisting heart disease diagnosis |
| CN111712186A (en) * | 2017-12-20 | 2020-09-25 | 医鲸股份有限公司 | Method and device for assisting in the diagnosis of cardiovascular diseases |
| CN109087691A (en) * | 2018-08-02 | 2018-12-25 | 科大智能机器人技术有限公司 | A kind of OTC drugs recommender system and recommended method based on deep learning |
| CN109326352A (en) * | 2018-10-26 | 2019-02-12 | 腾讯科技（深圳）有限公司 | Disease forecasting method, apparatus, terminal and storage medium |
| CN109326352B (en) * | 2018-10-26 | 2022-04-15 | 腾讯科技（深圳）有限公司 | Disease prediction method, device, terminal and storage medium |
| CN109616167A (en) * | 2018-12-12 | 2019-04-12 | 天津迈沃医药技术股份有限公司 | Doctor based on disease circle confuses method and system |
| CN109743363A (en) * | 2018-12-18 | 2019-05-10 | 广州圆爱康生物科技有限公司 | A kind of medical information intelligently pushing method and device |
| CN110379505A (en) * | 2019-06-10 | 2019-10-25 | 天津开心生活科技有限公司 | A kind of recognition methods, device, readable medium and the electronic equipment of the common processing mode of disease |
| CN111951955A (en) * | 2020-08-13 | 2020-11-17 | 神州数码医疗科技股份有限公司 | Method and device for constructing clinical decision support system based on rule reasoning |
| CN113160916A (en) * | 2021-05-14 | 2021-07-23 | 海南云信医疗科技有限公司 | Self-diagnosis system based on traditional Chinese medicine |
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