CN117116461A - Online personalized diagnosis and treatment evaluation method and system based on machine learning - Google Patents
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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Abstract
The application discloses an online personalized diagnosis and treatment evaluation method and system based on machine learning, and relates to the technical field of Internet medical treatment. The method comprises the following steps: account logging and identity acquisition, acquisition of diagnosis data, customization of a diagnosis model, personalized diagnosis and treatment, diagnosis and treatment evaluation and result viewing. The system is suitable for use in the method. By adopting the application, the user can finish the disease diagnosis of the user by uploading the diagnosis data through the user side and combining the diagnosis and treatment data, wherein the diagnosis and treatment background builds the diagnosis and treatment model of the user based on the diagnosis and treatment data, then carries out data matching on the diagnosis and treatment model of the user and the diagnosis and treatment database, so as to go to the diagnosis and treatment result, and generate the corresponding diagnosis and treatment evaluation list for the user to check at the user side.
Description
Technical Field
The application relates to the technical field of internet medical treatment, in particular to an online personalized diagnosis and treatment evaluation method and system based on machine learning.
Background
The birth of the Internet hospital provides convenience for patients. The health management service of the whole life cycle before, during and after diagnosis can be realized for the patient through the internet hospital auxiliary medical system. Meanwhile, hospital services can optimize and remodel the whole medical procedure of a patient through Internet hospital online.
At present, most online consultation is realized by online chatting between a patient and a doctor, and the consultation can be completed in time only by online simultaneous presence of the patient and the doctor, so that the online consultation is very inconvenient to use. Accordingly, there is a need for an on-line medical technique that can provide an on-line consultation that does not require a doctor to increase the convenience of use for the user.
Disclosure of Invention
The application aims to provide an on-line personalized diagnosis and treatment evaluation method and system based on machine learning, which are used for solving the technical problems in the background technology.
In order to achieve the above purpose, the present application discloses the following technical solutions:
the present application provides in a first aspect an on-line personalized diagnosis and treatment assessment method based on machine learning, the method comprising the steps of:
and (3) account login and identity acquisition: the user inputs login information through the user side, the user side verifies the login information and collects user identity data, and the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data;
acquiring diagnosis data: the diagnosis and treatment background collects diagnosis and treatment data of a user, wherein the diagnosis and treatment data comprises the diagnosis and treatment data, medical record contents uploaded by the user, image data uploaded by the user and a diagnosis report uploaded by the user;
customizing a diagnosis model: the diagnosis and treatment background builds a diagnosis and treatment model of the user based on the diagnosis and treatment data;
personalized diagnosis and treatment: the diagnosis and treatment background performs data matching on a diagnosis and treatment model of a user and a diagnosis and treatment database, so as to obtain diagnosis and treatment results, wherein the diagnosis and treatment database is obtained by performing machine learning by taking symptoms as keywords and combining medical big data;
diagnosis and treatment evaluation: the diagnosis and treatment background generates a diagnosis and treatment evaluation list based on the diagnosis and treatment result, wherein the diagnosis and treatment evaluation list records diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result;
results viewing: and the user invokes the diagnosis and treatment evaluation list through the user side to check the result.
Preferably, the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data, and specifically includes:
analyzing the user identity data by the diagnosis and treatment background, and extracting the unique diagnosis identification code granted to the user when registering or the user identification number;
the diagnosis and treatment background is used for matching the past medical records of the user in the diagnosis and treatment database based on the identification card number or the diagnosis and treatment identification code of the user, and the diagnosis and treatment database is stored with patient diagnosis records and medical records based on medical big data collection; and when the past medical record is matched with the past medical record duration, extracting the past medical record, wherein the diagnosis and treatment data comprise the past medical record.
Preferably, the customized doctor model specifically includes:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
Preferably, the consultation data further includes consultation data acquired based on an on-line consultation.
Preferably, the step of acquiring consultation data specifically includes:
the diagnosis and treatment background generates a question based on any one or more of the diagnosis and treatment data, medical record content uploaded by a user, image data uploaded by the user and a diagnosis report uploaded by the user, and the user gives an answer corresponding to the question;
and analyzing and acquiring the symptom expression of the user as the consultation data by the diagnosis and treatment background based on the answers corresponding to the inquiry questions.
Preferably, the inquiry questions include doctor-patient consultation questions and answer options generated based on medical big data, and the analyzing and acquiring the symptoms of the user specifically includes: the condition manifestation is obtained based on answer options selected by the user.
Preferably, the personalized diagnosis and treatment specifically includes:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
The application in a second aspect discloses an online personalized diagnosis and treatment evaluation system based on machine learning, which comprises a user side, a diagnosis and treatment database and a diagnosis and treatment platform;
the user terminal is configured to: for a user to register or log into the system, and is further configured to: collecting login information input by a user, verifying user identity data, and providing display of diagnosis and treatment evaluation sheets;
the diagnosis and treat database is configured to: taking the symptom expression as a keyword and combining medical big data to perform machine learning, so as to generate diagnosis and treatment results corresponding to the symptom expression;
the diagnosis and treatment background is configured to: collecting the diagnosis data of a user, constructing a diagnosis model of the user based on the diagnosis data, performing data matching on the diagnosis model of the user and the diagnosis database, and acquiring a diagnosis result corresponding to the diagnosis data; the diagnosis data comprise diagnosis and treatment data, medical record contents uploaded by a user, image data uploaded by the user and diagnosis reports uploaded by the user; is also configured to: based on the diagnosis and treatment result, a diagnosis and treatment evaluation list is generated, and diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result are recorded on the diagnosis and treatment evaluation list.
Preferably, the constructing the diagnosis model of the user specifically includes:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
Preferably, the data matching is performed on the diagnosis model of the user and the diagnosis database, and the diagnosis result corresponding to the diagnosis data is obtained, which specifically includes:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
The beneficial effects are that: according to the on-line personalized diagnosis and treatment evaluation method and system based on machine learning, a user uploads diagnosis data through a user side and combines the diagnosis and treatment data, so that disease diagnosis of the user can be completed, a diagnosis and treatment background builds a diagnosis and treatment model of the user based on the diagnosis and treatment data, then the diagnosis and treatment model of the user is subjected to data matching with a diagnosis and treatment database, so that diagnosis and treatment results are obtained later, a corresponding diagnosis and treatment evaluation list is generated for the user to check at the user side, in the diagnosis and treatment process, the user can complete on-line disease diagnosis, and in combination with the development of the technology of the Internet of things, the diagnosis and treatment results can be obtained quickly under the condition of having an electronic examination and treatment list, the use experience of the user is improved, and meanwhile, the reliability of the diagnosis and treatment results is ensured. Furthermore, the application combines personalized diagnosis and treatment steps through the analysis of head diseases and trunk diseases, and improves the reliability of diagnosis and treatment results. And further, through the generation of the inquiry problem, the effect of on-line diagnosis on the user under the condition of no inspection examination list is realized, and the practicability of the system is improved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an on-line personalized diagnosis and treatment evaluation method based on machine learning in an embodiment of the application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In this document, 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. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The embodiment discloses an online personalized diagnosis and treatment evaluation method based on machine learning as shown in fig. 1, which comprises the following steps:
s1-account login and identity acquisition: the user inputs login information through the user side, the user side verifies the login information and collects user identity data, and the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data;
s2, acquiring diagnosis data: the diagnosis and treatment background collects diagnosis and treatment data of a user, wherein the diagnosis and treatment data comprises the diagnosis and treatment data, medical record contents uploaded by the user, image data uploaded by the user and a diagnosis report uploaded by the user;
s3-customizing a diagnosis model: the diagnosis and treatment background builds a diagnosis and treatment model of the user based on the diagnosis and treatment data;
s4-personalized diagnosis and treatment: the diagnosis and treatment background performs data matching on a diagnosis and treatment model of a user and a diagnosis and treatment database, so as to obtain diagnosis and treatment results, wherein the diagnosis and treatment database is obtained by performing machine learning by taking symptoms as keywords and combining medical big data;
s5-diagnosis and treatment evaluation: the diagnosis and treatment background generates a diagnosis and treatment evaluation list based on the diagnosis and treatment result, wherein the diagnosis and treatment evaluation list records diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result;
s6, checking results: and the user invokes the diagnosis and treatment evaluation list through the user side to check the result.
In this embodiment, the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data, and specifically includes:
analyzing the user identity data by the diagnosis and treatment background, and extracting the unique diagnosis identification code granted to the user when registering or the user identification number;
the diagnosis and treatment background is used for matching the past medical records of the user in the diagnosis and treatment database based on the identification card number or the diagnosis and treatment identification code of the user, and the diagnosis and treatment database is stored with patient diagnosis records and medical records based on medical big data collection; and when the past medical record is matched with the past medical record duration, extracting the past medical record, wherein the diagnosis and treatment data comprise the past medical record.
In this embodiment, the customized diagnosis model specifically includes:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
Further preferably, the consultation data further includes consultation data acquired based on an on-line consultation. The consultation data acquisition step specifically comprises the following steps:
the diagnosis and treatment background generates a question based on any one or more of the diagnosis and treatment data, medical record content uploaded by a user, image data uploaded by the user and a diagnosis report uploaded by the user, and the user gives an answer corresponding to the question;
and analyzing and acquiring the symptom expression of the user as the consultation data by the diagnosis and treatment background based on the answers corresponding to the inquiry questions.
The inquiry questions comprise doctor-patient consultation questions and answer options generated based on medical big data, and the analyzing and acquiring the symptoms of the user specifically comprises the following steps: the condition manifestation is obtained based on answer options selected by the user.
In this embodiment, the personalized diagnosis and treatment specifically includes:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
The embodiment also provides an online personalized diagnosis and treatment evaluation system based on machine learning, which is suitable for the online personalized diagnosis and treatment evaluation method based on machine learning, and comprises a user side, a diagnosis and treatment database and a diagnosis and treatment platform.
Specifically, the client is configured to: for a user to register or log into the system, and is further configured to: collecting login information input by a user, verifying user identity data, and providing display of diagnosis and treatment evaluation sheets.
Specifically, the diagnosis and treatment database is configured to: and taking the symptom expression as a keyword and combining medical big data to perform machine learning, so as to generate diagnosis and treatment results corresponding to the symptom expression.
Specifically, the diagnosis and treatment background is configured to: collecting the diagnosis data of a user, constructing a diagnosis model of the user based on the diagnosis data, performing data matching on the diagnosis model of the user and the diagnosis database, and acquiring a diagnosis result corresponding to the diagnosis data; the diagnosis data comprise diagnosis and treatment data, medical record contents uploaded by a user, image data uploaded by the user and diagnosis reports uploaded by the user; is also configured to: based on the diagnosis and treatment result, a diagnosis and treatment evaluation list is generated, and diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result are recorded on the diagnosis and treatment evaluation list.
As a possible implementation manner of this embodiment, the constructing a diagnosis model of the user specifically includes:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
As a possible implementation manner of this embodiment, the data matching between the diagnosis model of the user and the diagnosis database, and obtaining the diagnosis result corresponding to the diagnosis data specifically includes:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
It should be noted that, the on-line personalized diagnosis and treatment evaluation system based on machine learning provided in this embodiment corresponds to the above-mentioned on-line personalized diagnosis and treatment evaluation method based on machine learning, so that other functions/implementation methods of each component of the system may refer to the above-mentioned on-line personalized diagnosis and treatment evaluation method based on machine learning, and therefore, the description thereof is omitted herein.
In the embodiments provided by the present application, it is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For a hardware implementation, the processor may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the flow of an embodiment may be accomplished by a computer program to instruct the associated hardware. When implemented, the above-described programs may be stored in or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. The computer-readable storage media may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present application, and although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present application.
Claims (10)
1. An online personalized diagnosis and treatment evaluation method based on machine learning is characterized by comprising the following steps:
and (3) account login and identity acquisition: the user inputs login information through the user side, the user side verifies the login information and collects user identity data, and the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data;
acquiring diagnosis data: the diagnosis and treatment background collects diagnosis and treatment data of a user, wherein the diagnosis and treatment data comprises the diagnosis and treatment data, medical record contents uploaded by the user, image data uploaded by the user and a diagnosis report uploaded by the user;
customizing a diagnosis model: the diagnosis and treatment background builds a diagnosis and treatment model of the user based on the diagnosis and treatment data;
personalized diagnosis and treatment: the diagnosis and treatment background performs data matching on a diagnosis and treatment model of a user and a diagnosis and treatment database, so as to obtain diagnosis and treatment results, wherein the diagnosis and treatment database is obtained by performing machine learning by taking symptoms as keywords and combining medical big data;
diagnosis and treatment evaluation: the diagnosis and treatment background generates a diagnosis and treatment evaluation list based on the diagnosis and treatment result, wherein the diagnosis and treatment evaluation list records diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result;
results viewing: and the user invokes the diagnosis and treatment evaluation list through the user side to check the result.
2. The machine learning based on-line personalized diagnosis and treatment evaluation method according to claim 1, wherein the diagnosis and treatment background matches diagnosis and treatment data based on the user identity data, and specifically comprises:
analyzing the user identity data by the diagnosis and treatment background, and extracting the unique diagnosis identification code granted to the user when registering or the user identification number;
the diagnosis and treatment background is used for matching the past medical records of the user in the diagnosis and treatment database based on the identification card number or the diagnosis and treatment identification code of the user, and the diagnosis and treatment database is stored with patient diagnosis records and medical records based on medical big data collection; and when the past medical record is matched with the past medical record duration, extracting the past medical record, wherein the diagnosis and treatment data comprise the past medical record.
3. The machine learning based on-line personalized diagnosis and treatment assessment method according to claim 1, wherein the customized diagnosis and treatment model specifically comprises:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
4. The machine learning based on-line personalized diagnosis and treat evaluation method of claim 3, wherein the diagnosis data further comprises consultation data acquired based on-line consultation.
5. The machine learning based on-line personalized diagnosis and treat evaluation method according to claim 4, wherein the step of acquiring the consultation data specifically comprises:
the diagnosis and treatment background generates a question based on any one or more of the diagnosis and treatment data, medical record content uploaded by a user, image data uploaded by the user and a diagnosis report uploaded by the user, and the user gives an answer corresponding to the question;
and analyzing and acquiring the symptom expression of the user as the consultation data by the diagnosis and treatment background based on the answers corresponding to the inquiry questions.
6. The machine learning based on-line personalized diagnosis and treat evaluation method according to claim 5, wherein the inquiry questions comprise doctor-patient consultation questions and answer options generated based on medical big data, and the analyzing to obtain the symptoms of the user comprises: the condition manifestation is obtained based on answer options selected by the user.
7. The machine learning based on-line personalized diagnosis and treat evaluation method according to claim 3, wherein the personalized diagnosis and treat specifically comprises:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
8. The on-line personalized diagnosis and treatment evaluation system based on machine learning is characterized by comprising a user side, a diagnosis and treatment database and a diagnosis and treatment platform;
the user terminal is configured to: for a user to register or log into the system, and is further configured to: collecting login information input by a user, verifying user identity data, and providing display of diagnosis and treatment evaluation sheets;
the diagnosis and treat database is configured to: taking the symptom expression as a keyword and combining medical big data to perform machine learning, so as to generate diagnosis and treatment results corresponding to the symptom expression;
the diagnosis and treatment background is configured to: collecting the diagnosis data of a user, constructing a diagnosis model of the user based on the diagnosis data, performing data matching on the diagnosis model of the user and the diagnosis database, and acquiring a diagnosis result corresponding to the diagnosis data; the diagnosis data comprise diagnosis and treatment data, medical record contents uploaded by a user, image data uploaded by the user and diagnosis reports uploaded by the user; is also configured to: based on the diagnosis and treatment result, a diagnosis and treatment evaluation list is generated, and diagnosis and treatment data, the diagnosis and treatment result, a disease evaluation result and a disease treatment means corresponding to the diagnosis and treatment result are recorded on the diagnosis and treatment evaluation list.
9. The machine learning based on-line personalized diagnosis and treatment evaluation system according to claim 8, wherein the constructing the user's diagnosis and treatment model specifically comprises:
analyzing the diagnosis and treatment data based on a natural language processing technology, and extracting disease feature data in the diagnosis and treatment data;
classifying the disease characteristic data to obtain a disease type corresponding to each disease characteristic data;
based on the acquired symptom type, matching with preset types in a model database, acquiring a head symptom and a plurality of trunk symptoms, wherein the head symptom is at least one symptom with strongest symptom expression, and the trunk symptoms comprise symptoms concurrent with the head symptom and/or symptoms which are expressed on a user and except for the head symptom;
a diagnosis model of the user is constructed based on the head disorder and the torso disorder.
10. The machine learning based on-line personalized diagnosis and treatment evaluation system according to claim 9, wherein the data matching the diagnosis and treatment model of the user with the diagnosis and treatment database, and obtaining the diagnosis and treatment result corresponding to the diagnosis and treatment data, specifically comprises:
the diagnosis and treatment background extracts head symptoms and trunk symptoms corresponding to the diagnosis and treatment model;
performing data matching on the head symptoms and the trunk symptoms with symptoms preset in the diagnosis and treatment database, and acquiring a characterization weight coefficient delta corresponding to the symptoms;
comparing all the head symptoms with the characterization weight coefficients delta corresponding to the trunk symptoms, extracting one or more corresponding head symptoms and/or trunk symptoms with the largest characterization weight coefficient delta as critical symptoms, and generating the diagnosis and treatment result.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021910A (en) * | 2016-05-17 | 2016-10-12 | 重庆医科大学附属永川医院 | Intelligent medical service-based remote disease diagnosis system |
| CN109599174A (en) * | 2018-11-15 | 2019-04-09 | 李慧 | Tele-medicine control method, system and equipment |
| CN113506625A (en) * | 2021-08-21 | 2021-10-15 | 江泽飞 | Diagnosis and treatment suggestion scoring system based on csco guide |
| CN113936762A (en) * | 2021-09-21 | 2022-01-14 | 姜昶 | Intelligent medical treatment data storage method and platform based on block chain |
| CN114334070A (en) * | 2022-01-05 | 2022-04-12 | 上海良方健康科技有限公司 | Auxiliary prescription system based on medical big data |
| CN116453666A (en) * | 2023-04-21 | 2023-07-18 | 河南农业大学 | Internet-based consultation and post-consultation management system |
-
2023
- 2023-09-05 CN CN202311136848.2A patent/CN117116461A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021910A (en) * | 2016-05-17 | 2016-10-12 | 重庆医科大学附属永川医院 | Intelligent medical service-based remote disease diagnosis system |
| CN109599174A (en) * | 2018-11-15 | 2019-04-09 | 李慧 | Tele-medicine control method, system and equipment |
| CN113506625A (en) * | 2021-08-21 | 2021-10-15 | 江泽飞 | Diagnosis and treatment suggestion scoring system based on csco guide |
| CN113936762A (en) * | 2021-09-21 | 2022-01-14 | 姜昶 | Intelligent medical treatment data storage method and platform based on block chain |
| CN114334070A (en) * | 2022-01-05 | 2022-04-12 | 上海良方健康科技有限公司 | Auxiliary prescription system based on medical big data |
| CN116453666A (en) * | 2023-04-21 | 2023-07-18 | 河南农业大学 | Internet-based consultation and post-consultation management system |
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