CN108932975A - A kind of recommender system based on mixing collaborative filtering doctors and patients information - Google Patents
A kind of recommender system based on mixing collaborative filtering doctors and patients information Download PDFInfo
- Publication number
- CN108932975A CN108932975A CN201810788894.3A CN201810788894A CN108932975A CN 108932975 A CN108932975 A CN 108932975A CN 201810788894 A CN201810788894 A CN 201810788894A CN 108932975 A CN108932975 A CN 108932975A
- Authority
- CN
- China
- Prior art keywords
- doctor
- information
- patient
- similarity
- score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H40/20—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 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Biomedical Technology (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the invention discloses a kind of recommender systems based on mixing collaborative filtering doctors and patients information, wherein the system includes: database module, for storing patient information, information about doctor, patient to the score information of doctor；Similarity calculation module, carries out being converted to vector for information nonumeric in the information to database purchase, carries out calculating similarity to all information vectors；Recommending module calculates prediction scoring for carrying out according to similarity data, and the highest top n doctor that will finally score carries out recommending target patient；Feedback module, for feeding back to the scoring for receiving the medical skill progress that the target patient after treating is directed to corresponding doctor in corresponding doctor's evaluation information library.Implement the embodiment of the present invention, be able to solve conventional recommendation systems cold start-up the problem of, improve the accuracy of recommendation information, simultaneously, reduce consumption to computing resource while guaranteeing to recommend accuracy, improve the response speed of system, enhance the experience sense of user by.
Description
Technical field
The present invention relates to recommender system technical field more particularly to a kind of recommendations based on mixing collaborative filtering doctors and patients information
System.
Background technique
" the difficulty of getting medical service " is one of current medical field urgent problem to be solved.The main reason for causing " the difficulty of getting medical service " phenomenon
There are two, it is on the one hand the deficiency of China's medical resource, another aspect patient does not have effective approach to find suitable medical treatment money
Source.The insufficient of medical resource needs country to increase the investment to medical treatment, and is that patient recommends suitable medical resource, so as to improve
" the difficulty of getting medical service " this problem is the problem of we can solve at present.The recommendation of medical resource is mainly the recommendation of hospital, doctor,
The present invention is based on this, recommends suitable this project of doctor to propose a solution for for patient.
For finding suitable this problem of doctor, patient is often difficult to find suitable doctor to be diagnosed for it.Main cause
There are many, patient can only often learn the rough type of illnesses and be difficult to have specific type belonging to oneself disease first
More accurately judgement, this also can not just find most suitable doctor；In addition the approach of patient's selection doctor is more single is usually
Online inquiry or kith and kin recommend, and the method for this selection doctor is often difficult more blindly not according to the actual conditions of oneself
Find more appropriate doctor.
Current oriented patient recommends one of the system of doctor, as long as being pushed away using the collaborative filtering method based on patient
It recommends, the scoring according to patient to doctor, similarity calculating method calculates other patients of target patient using Pearson correlation coefficient
Similarity, gathered based on similar patients and calculate target patient and score the prediction of the doctor that do not score, finally by linear combination
Prediction scoring, doctor's ability scoring, the overall score that distance scores to the end, and by the preceding N doctor of overall score be patient into
Row is recommended.But when new patient does not score to any doctor, which can not recommend for new patient.Therefore the system
There is the problem of user's cold start-up.On the other hand, which uses the collaborative filtering based on patient, when system data scale increases
When patient increases significantly, system, which calculates the similarity between patient according to scoring vector, will consume a large amount of computing resource, and meeting
The response speed of reduction system.Meanwhile the system uses Pearson correlation coefficient, the calculation method in patient's similarity calculation
Excessively complicated and time consumption influences user experience when the response time that the increase of system data amount will lead to system lengthens.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, and the present invention provides one kind based on mixing collaborative filtering letter
The recommender system of breath carries out effective group by the prediction scoring for generating the prediction scoring of collaborative filtering with content-based recommendation
It closes, solves the problems, such as conventional recommendation systems cold start-up, improve the accuracy of recommendation information, meanwhile, guaranteeing to recommend accurately
Reduce the consumption to computing resource while spending, improve the response speed of system, enhance the experience sense of user by.
To solve the above-mentioned problems, the invention proposes it is a kind of based on mixing collaborative filtering doctors and patients information recommender system,
The system comprises:
Database module, for storing patient information, information about doctor, patient to the score information of doctor；
Similarity calculation module carries out being converted to vector, to institute for information nonumeric in the information to database purchase
There is information vector to carry out calculating similarity；
Recommending module calculates prediction scoring for carrying out according to similarity data, and will finally score highest top n doctor
Life carries out recommending target patient；
Feedback module, for feeding back to the scoring for receiving the medical skill progress that the target patient after treating is directed to corresponding doctor
In corresponding doctor's evaluation information library.
Preferably, the system also includes:
Acquisition module, for acquiring patient information, information about doctor using mobile terminal；
Transmission module, for carrying out the patient information and information about doctor to be transported to database in the way of Bluetooth transmission
In.
Preferably, the mobile terminal can be the equipment such as bracelet, mobile phone, tablet computer.
Preferably, data base management system uses MySql system in the database module.
Preferably, the patient information underlying attribute includes: User ID, height, weight, gender, age, blood platelet correlation
Supplemental characteristic, red blood cell associated parameter data, enzyme associated parameter data, illness type, illness symptom.
Preferably, the information about doctor underlying attribute includes: that doctor ID, the affiliated hospital of doctor, doctor academic title, doctor are good at
Subject, operation experience, doctor's educational background, doctor's gender.
The patient includes: patient ID, doctor ID, score value to the score information of doctor.
Preferably, the similarity calculation module includes:
Nonumeric information conversion unit, for carrying out conversion vector to nonumeric information in the patient information；
Patient's similarity calculated, for calculating target patient in the similarity of other patients；Wherein, when certain features
When missing, lack part in two vectors is rejected when calculating similarity.
Doctor's similarity calculated, for being counted by the sequence scored from high to low according to patient to doctor
Calculate similarity；Wherein, when certain user does not score to doctor, two vectors give up this part in calculating.
Comparison diagram unit, for corresponding similarity analysis figure to be made according to the similarity of patient and the similarity of doctor
Table.
Preferably, the recommending module includes:
Patient's matching primitives unit obtains the K for basis and the maximum preceding K patient of target patient similarity
Patient carries out the prediction scoring for calculating other patients similar with target patient to doctor to the history scoring of doctor；
Doctor inquires computing unit, for basis and the maximum preceding K doctor of doctor's similarity to be scored, obtains the mesh
Mark patient carries out the prediction scoring for calculating target patient to the doctor that do not score to the history scoring of other doctors；
Collaborative filtering unit, for other patients similar with target patient according to the prediction scoring of doctor and institute
It states target patient and calculating filtering is carried out to the prediction of the doctor that do not score scoring, obtain final prediction scoring according to arranging from high to low
Sequence；
Recommendation unit, for final score, highest top n doctor recommends target patient.
Preferably, the system is judged before carrying out the recommending module for target patient personal information, works as target
Patient is to carry out retrieval doctor for the first time to score, then directly carries out doctor and inquire computing unit, patient's matching primitives unit, collaboration
Filter element and recommendation unit；When target patient is not to carry out retrieval doctor for the first time to score, then directly progress patient matches meter
Calculate unit and recommendation unit.
Implement the embodiment of the present invention, be able to solve conventional recommendation systems cold start-up the problem of, improve recommendation information
Accuracy, meanwhile, guarantee to recommend to reduce the consumption to computing resource while accuracy, improves the response speed of system
Degree, enhance the experience sense of user by.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of structure composition signal of the recommender system based on mixing collaborative filtering information in the embodiment of the present invention
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 is a kind of structure composition signal of recommender system based on mixing collaborative filtering information of the embodiment of the present invention
Figure, as shown in Figure 1, the system includes:
Database module, for storing patient information, information about doctor, patient to the score information of doctor；
Similarity calculation module carries out being converted to vector, to institute for information nonumeric in the information to database purchase
There is information vector to carry out calculating similarity；
Recommending module calculates prediction scoring for carrying out according to similarity data, and will finally score highest top n doctor
Life carries out recommending target patient；
Feedback module, for feeding back to the scoring for receiving the medical skill progress that the target patient after treating is directed to corresponding doctor
In corresponding doctor's evaluation information library.
The system also includes:
Acquisition module, for acquiring patient information, information about doctor using mobile terminal；
Transmission module, for carrying out the patient information and information about doctor to be transported to database in the way of Bluetooth transmission
In.
The mobile terminal can be the equipment such as bracelet, mobile phone, tablet computer.
Database module is described further:
Data base management system uses MySql system in the database module；
The patient information underlying attribute includes: User ID, height, weight, gender, age, platelet parameter number
According to, red blood cell associated parameter data, enzyme associated parameter data, illness type, illness symptom；
The information about doctor underlying attribute includes: that doctor ID, the affiliated hospital of doctor, doctor academic title, doctor are good at subject, hold
It goes through already, doctor's educational background, doctor's gender；
The patient includes: patient ID, doctor ID, score value to the score information of doctor.
Similarity calculation module is described further:
The similarity calculation module includes:
Nonumeric information conversion unit, for carrying out conversion vector to nonumeric information in the patient information；Such as: to property
[male, female] is not transformed into [1,0], obtains patient characteristic vector.Such as patient characteristic height 180cm, weight 60kg, gender male, year
Age 25, platelet parameter data, that is, platelet count, blood platelet evaluation volume, glycoprotein Ⅵ (166,9.9,
17.4), erythrocyte parameter data, that is, hematocrit, red blood cell evaluation volume, erythrocyte distribution width (0.479,91.9,
31.9), enzyme supplemental characteristic, that is, Aspartate amino converting Enzyme, alanine aminotransferase (24.96,23.1), illness type
(gastric ulcer, 9), illness symptom (have a stomach-ache, 19), corresponding feature vector be [180,60,1,25,166,9.9,17.4,
0.479,91.9,31.9,24.96,23.1,9,19].
Patient's similarity calculated, for calculating target patient in the similarity of other patients；Wherein, when certain features
When missing, lack part in two vectors is rejected when calculating similarity.
Doctor's similarity calculated, for being counted by the sequence scored from high to low according to patient to doctor
Calculate similarity；Wherein, when certain user does not score to doctor, two vectors give up this part in calculating.Such as, by institute
There is patient to can be used as a vector to the scoring of some doctor, it is assumed that three patients are (5,6,8) to the scoring of doctor a,
And these three patients are (1,3,4) to the scoring of doctor b, by calculating it is believed that two doctor's dissmilarities.And if these three
Patient is that (5,6,7) then think that a, two doctor of b are similar to the scoring of doctor b.
Comparison diagram unit, for corresponding similarity analysis chart to be made according to the similarity of patient and the similarity of doctor
A、B。
The recommending module is described further:
Patient's matching primitives unit obtains the K for basis and the maximum preceding K patient of target patient similarity
Patient carries out the prediction scoring for calculating other patients similar with target patient to doctor to the history scoring of doctor；
Doctor inquires computing unit, for basis and the maximum preceding K doctor of doctor's similarity to be scored, obtains the mesh
Mark patient carries out the prediction scoring for calculating target patient to the doctor that do not score to the history scoring of other doctors；
Collaborative filtering unit, for other patients similar with target patient according to the prediction scoring of doctor and institute
It states target patient and calculating filtering is carried out to the prediction of the doctor that do not score scoring, obtain final prediction scoring according to arranging from high to low
Sequence；
Recommendation unit, for final score, highest top n doctor recommends target patient.
The system is judged before carrying out the recommending module for target patient personal information, when target patient is first
It is secondary carrying out retrieval doctor's scoring, then it directly carries out doctor and inquires computing unit, patient's matching primitives unit, collaborative filtering unit
And recommendation unit；When target patient be not carry out for the first time retrieval doctor score, then directly progress patient's matching primitives unit and
Recommendation unit.
Implement the embodiment of the present invention, be able to solve conventional recommendation systems cold start-up the problem of, improve recommendation information
Accuracy, meanwhile, guarantee to recommend to reduce the consumption to computing resource while accuracy, improves the response speed of system
Degree, enhance the experience sense of user by.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage
Medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random
Access Memory), disk or CD etc..
In addition, being provided for the embodiments of the invention a kind of recommender system based on mixing collaborative filtering doctors and patients information above
It is described in detail, used herein a specific example illustrates the principle and implementation of the invention, the above reality
The explanation for applying example is merely used to help understand method and its core concept of the invention；Meanwhile for the general technology of this field
Personnel, according to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion this theory
Bright book content should not be construed as limiting the invention.
Claims (6)
1. a kind of recommender system based on mixing collaborative filtering doctors and patients information, which is characterized in that the system comprises:
Database module, for storing patient information, information about doctor, patient to the score information of doctor；
Similarity calculation module carries out being converted to vector, to all letters for information nonumeric in the information to database purchase
Breath vector carries out calculating similarity；
Recommending module, for calculate prediction scoring according to similarity data, will finally score highest top n doctor into
Row recommends target patient；
Feedback module, for the scoring for receiving the medical skill progress that the target patient after treating is directed to corresponding doctor to be fed back to correspondence
Doctor's evaluation information library in.
2. a kind of recommender system based on mixing collaborative filtering doctors and patients information according to claim 1, which is characterized in that institute
State system further include:
Acquisition module, for acquiring patient information, information about doctor using mobile terminal；
Transmission module, for be transported in database by the patient information and information about doctor in the way of Bluetooth transmission.
3. a kind of recommender system based on mixing collaborative filtering doctors and patients information according to claim 1, which is characterized in that institute
Stating patient information underlying attribute includes: User ID, height, weight, gender, age, platelet parameter data, red blood cell phase
Close supplemental characteristic, enzyme associated parameter data, illness type, illness symptom；The information about doctor underlying attribute include: doctor ID,
The affiliated hospital of doctor, doctor academic title, doctor are good at subject, operation experience, doctor's educational background, doctor's gender；The patient is to doctor
Score information include: patient ID, doctor ID, score value.
4. a kind of recommender system based on mixing collaborative filtering doctors and patients information according to claim 1, which is characterized in that institute
Stating similarity calculation module includes:
Nonumeric information conversion unit, for carrying out conversion vector to nonumeric information in the patient information；
Patient's similarity calculated, for calculating target patient in the similarity of other patients；Wherein, when certain features lack
When, lack part in two vectors is rejected when calculating similarity.
Doctor's similarity calculated, for carrying out calculating phase by the sequence scored from high to low according to patient to doctor
Like degree；Wherein, when certain user does not score to doctor, two vectors give up this part in calculating.
Comparison diagram unit, for corresponding similarity analysis chart to be made according to the similarity of patient and the similarity of doctor.
5. a kind of recommender system based on mixing collaborative filtering doctors and patients information according to claim 1, which is characterized in that institute
Stating recommending module includes:
Patient's matching primitives unit obtains the K patient for basis and the maximum preceding K patient of target patient similarity
The prediction scoring for calculating other patients similar with target patient to doctor is carried out to the history scoring of doctor；
Doctor inquires computing unit, for basis and the maximum preceding K doctor of doctor's similarity to be scored, obtains the target and suffers from
Person carries out the prediction scoring for calculating target patient to the doctor that do not score to the history scoring of other doctors；
Collaborative filtering unit, for other patients similar with target patient according to the prediction scoring of doctor and the mesh
Mark patient carries out calculating filtering to the prediction of the doctor that do not score scoring, obtains final prediction scoring according to sorting from high to low；
Recommendation unit, for final score, highest top n doctor recommends target patient.
6. a kind of recommender system based on mixing collaborative filtering doctors and patients information according to claim 5, which is characterized in that institute
It states before system carries out the recommending module and is judged for target patient personal information, when target patient is to be retrieved for the first time
Doctor's scoring, then it directly carries out doctor and inquires computing unit, patient's matching primitives unit, collaborative filtering unit and recommend single
Member；When target patient is not to carry out retrieval doctor for the first time score, then direct progress patient's matching primitives unit and recommendation unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810788894.3A CN108932975A (en) | 2018-07-18 | 2018-07-18 | A kind of recommender system based on mixing collaborative filtering doctors and patients information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810788894.3A CN108932975A (en) | 2018-07-18 | 2018-07-18 | A kind of recommender system based on mixing collaborative filtering doctors and patients information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108932975A true CN108932975A (en) | 2018-12-04 |
Family
ID=64447655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810788894.3A Pending CN108932975A (en) | 2018-07-18 | 2018-07-18 | A kind of recommender system based on mixing collaborative filtering doctors and patients information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108932975A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110993081A (en) * | 2019-12-03 | 2020-04-10 | 济南大学 | Doctor online recommendation method and system |
| CN111191020A (en) * | 2019-12-27 | 2020-05-22 | 江苏省人民医院（南京医科大学第一附属医院） | Prescription recommendation method and system based on machine learning and knowledge graph |
| CN111415760A (en) * | 2020-03-26 | 2020-07-14 | 潘佳亮 | Doctor recommendation method, system, computer equipment and storage medium |
| CN113010783A (en) * | 2021-03-17 | 2021-06-22 | 华南理工大学 | Medical recommendation method, system and medium based on multi-modal cardiovascular disease information |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106339578A (en) * | 2016-08-17 | 2017-01-18 | 秦皇岛市第医院 | Multi-strategy integration hospital patient registration recommendation method |
| CN107680660A (en) * | 2016-07-27 | 2018-02-09 | 百度在线网络技术（北京）有限公司 | Recommend the method and apparatus of doctor |
| CN107705842A (en) * | 2017-10-13 | 2018-02-16 | 合肥工业大学 | Intelligent system for distribution of out-patient department and its method of work |
| CN108039198A (en) * | 2017-12-11 | 2018-05-15 | 重庆邮电大学 | A kind of doctor towards portable medical recommends method and system |
-
2018
- 2018-07-18 CN CN201810788894.3A patent/CN108932975A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107680660A (en) * | 2016-07-27 | 2018-02-09 | 百度在线网络技术（北京）有限公司 | Recommend the method and apparatus of doctor |
| CN106339578A (en) * | 2016-08-17 | 2017-01-18 | 秦皇岛市第医院 | Multi-strategy integration hospital patient registration recommendation method |
| CN107705842A (en) * | 2017-10-13 | 2018-02-16 | 合肥工业大学 | Intelligent system for distribution of out-patient department and its method of work |
| CN108039198A (en) * | 2017-12-11 | 2018-05-15 | 重庆邮电大学 | A kind of doctor towards portable medical recommends method and system |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110993081A (en) * | 2019-12-03 | 2020-04-10 | 济南大学 | Doctor online recommendation method and system |
| CN110993081B (en) * | 2019-12-03 | 2023-08-11 | 济南大学 | Doctor online recommendation method and system |
| CN111191020A (en) * | 2019-12-27 | 2020-05-22 | 江苏省人民医院（南京医科大学第一附属医院） | Prescription recommendation method and system based on machine learning and knowledge graph |
| CN111191020B (en) * | 2019-12-27 | 2023-09-22 | 江苏省人民医院（南京医科大学第一附属医院） | Prescription recommendation method and system based on machine learning and knowledge graph |
| CN111415760A (en) * | 2020-03-26 | 2020-07-14 | 潘佳亮 | Doctor recommendation method, system, computer equipment and storage medium |
| CN111415760B (en) * | 2020-03-26 | 2023-10-13 | 潘佳亮 | Doctor recommendation method, doctor recommendation system, computer equipment and storage medium |
| CN113010783A (en) * | 2021-03-17 | 2021-06-22 | 华南理工大学 | Medical recommendation method, system and medium based on multi-modal cardiovascular disease information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11621077B2 (en) | Methods and systems for using artificial intelligence to select a compatible element | |
| Thompson et al. | The Ottawa Charter 30 years on: still an important standard for health promotion | |
| Gratton et al. | The 100-year life: Living and working in an age of longevity | |
| Rabbi et al. | Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults | |
| CN108932975A (en) | A kind of recommender system based on mixing collaborative filtering doctors and patients information | |
| Baum | What judges want: Judges' goals and judicial behavior | |
| Hartmann et al. | Organizational commitment: A multi method scale analysis and test of effects | |
| Heft | An ecological approach to psychology | |
| Lukaszewski | Testing an adaptationist theory of trait covariation: Relative bargaining power as a common calibrator of an interpersonal syndrome | |
| Güveli et al. | The Rise ofNew'Social Classes within the Service Class in The Netherlands: Political Orientation of Social and Cultural Specialists and Technocrats between 1970 and 2003 | |
| CN110085307A (en) | A kind of intelligent hospital guide's method and system based on the fusion of multi-source knowledge mapping | |
| JP5727706B2 (en) | Lifestyle improvement support system, lifestyle improvement support method and program | |
| Bakker et al. | Inequity in work and intimate relationships: A spillover–crossover model | |
| US20160049092A1 (en) | Managing body composition | |
| Moon et al. | Enrolment and programmatic trends and predictors of antiretroviral therapy initiation from president's emergency plan for AIDS Relief (PEPFAR)-supported public HIV care and treatment sites in rural Mozambique | |
| Keefer et al. | Time–space distanciation: An empirically supported integrative framework for the cultural psychology of time and space | |
| CN109509117A (en) | A kind of vegetable recommended method, apparatus and system | |
| Barkworth et al. | Giving at risk? Examining perceived risk and blood donation behaviour | |
| Chen et al. | What concerns consumers about hypertension? A comparison between the online health community and the Q&A forum | |
| Clinch et al. | Learning from our mistakes: Identifying opportunities for technology intervention against everyday cognitive failure | |
| WO2020132478A1 (en) | Diet quality fingerprinting | |
| Gratzer | The cure: how capitalism can save American health care | |
| Ramakrishnan | Being a young pediatric cardiologist in India: Aspirations versus reality | |
| US20110145740A1 (en) | Predictive Performance Calculator Apparatus and Method | |
| Xu et al. | The development of spatial cognition and its malleability assessed in mass population via a mobile game |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181204 |
|
| RJ01 | Rejection of invention patent application after publication |