CN106295147A - The medical expert's personalized recommendation method solved based on big data and system - Google Patents
The medical expert's personalized recommendation method solved based on big data and system Download PDFInfo
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- CN106295147A CN106295147A CN201610624958.7A CN201610624958A CN106295147A CN 106295147 A CN106295147 A CN 106295147A CN 201610624958 A CN201610624958 A CN 201610624958A CN 106295147 A CN106295147 A CN 106295147A
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- 238000012986 modification Methods 0.000 claims description 15
- 230000004048 modification Effects 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 9
- 238000012797 qualification Methods 0.000 claims description 6
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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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Abstract
The invention discloses a kind of medical expert's personalized recommendation method solved based on big data and system, the method comprises the steps: S1, sets up Medical Technologist data base, and Medical Technologist data base includes that expert's personal information, patient are to expert opinion data, hospital's data；S2, by big data method multi-party verification expert's personal information；S3, the speciality recommendation degree of calculating expert；S4, by big data method checking patient to expert opinion data；S5, by big data method checking hospital data；S6, according to above-mentioned three aspect comprehensive modelings, set up expert's speciality recommended models；S7, acquisition patient information；S8, set up individual demand model according to patient information；S9, carry out personalized recommendation medical expert according to expert's speciality recommended models, individual demand model.The present invention solves doctors and patients' information asymmetry contradiction, improves patient and seeks medical advice precision, and abundant Appropriate application medical resource, it is achieved personalization of seeking medical advice improves clinical efficacy.
Description
Technical field
The present invention relates to field of medical technology, be specifically related to a kind of medical expert's personalized recommendation solved based on big data
Method and system.
Background technology
The therapeutic effect of patient is affected by many factors, and the most whether finds optimal expert, is most important factor.
Owing to medical knowledge is not known about by patient, the speciality of medical institutions and expert is not known about, when selecting expert,
Usually it is in blindly passive state, this information asymmetry, has a strong impact on the effect of seeking medical advice of patient, if expert has to examine
Treat the disease that some are not oneself specialities, be also a kind of waste to expert, do not play expert's speciality, affect quality of medical care.
Different hospital technical merit there are differences, and Different hospital has different characteristic speciality section office, and hospital of same institute is different
Training level existence difference, same training, different doctors there is also difference, show each have their own speciality, so, Huan Zheyao
Precisely select, extremely difficult.
Patient is when selecting expert, and general patient is to select famous hospital, but the training selected may not this doctor
The Priority Department of institute；Some patient of specialty selects famous training again, but section indoor doctor is respectively arranged with speciality, even if selecting section office master
Appoint, may oneself speciality the most non-to the disease of this patient；More professional patient selects expert, by network inquiry, understands
The data of expert and other people evaluation, but, dragons and fishes jumbled together for the present network information, a lot of poor information accuracys, Huan Zhewu
Method is screened.So, in the urgent need to the proposed algorithm of a kind of objective and fair, help patient to find the doctor of real specialty.
On the other hand, different patients, even if suffering from same disease, its diagnosis and treatment project is the most different, and different experts is i.e.
Making same disease, also monograph is also not quite similar in different diagnosis and treatment schemes, its effect.Such as, in shaping and beauty industry, with
Sample is Inent M Andibular Angle deformity, is being the expert that Inent M Andibular Angle treatment field is well-known, and have is good at osteotomy, and have is good at mill
Bone art, what modus operandi also had is good at postauricular incision, and have is good at Intraoral incision.So, how according to patient characteristic's feature and
Demand, finds most suitable expert, is also the urgent needs of patient.
Additionally, medical industry is maked rapid progress, new technique, new therapy emerge in an endless stream, and expert is also in renewal of constantly growing up, once
The expert in a certain field, may be substituted by new technique, patient cannot fully understand these information, is also to cause medical effect
One of the best factor.It is therefore desirable to set up a kind of recommendation method of real-time update, allow patient obtains is up-to-date, best
Therapy.
Summary of the invention
In view of this, cannot find be best suitable for the technical problem of oneself medical expert to solve patient in prior art,
The present invention proposes a kind of medical expert's personalized recommendation method solved based on big data and system, and make full use of internet letter
Breath, by big data method, sets up a set of technology and algorithm, needs from the individual character of professional speciality, service quality and the patient of expert
Ask, set up personalized expert and recommend, abundant Appropriate application medical resource, improves patient's treatment effect.
The present invention solves the problems referred to above by techniques below means:
A kind of medical expert's personalized recommendation method solved based on big data, comprises the steps:
S1, setting up Medical Technologist data base, Medical Technologist data base includes that expert's personal information, patient are to expert opinion
Data, hospital's data；
S2, by big data method multi-party verification expert's personal information；
S3, the speciality recommendation degree of calculating expert；
S4, by big data method checking patient to expert opinion data；
S5, by big data method checking hospital data；
S6, according to above-mentioned three aspect comprehensive modelings, set up expert's speciality recommended models；
S7, acquisition patient information；
S8, set up individual demand model according to patient information；
S9, carry out personalized recommendation medical expert according to expert's speciality recommended models, individual demand model.
Further, step S1 specifically includes following steps:
S11, by portal website of each hospital collect expert's personal information；
Modification and perfection personal information after S12, expert's certification；
S13, by third party website obtain patient to expert opinion data；
S14, obtain hospital data by Hospital Website.
Further, in step S1, expert's personal information includes expert's basic document, educational background, degree, academic title, specially
Industry academic title, post, mentorship, speciality, education experience, work experience, deliver treatise, obtain problem, achievement, award, science
Tenure.
Further, in step S2, multi-party verification includes that constituent parts website authentication admitted patient's information, each association website are tested
The academic tenure information of card, fund website authentication topic information, domestic and foreign databases are verified treatise information, are defended planning commission's website authentication money
Lattice card information, letter net checking educational background degree.
Further, step S3 specifically includes following steps:
Note:
FieldRAcademic title=F (professor；Associate professor；Lecturer；Assiatant；Nothing)
FieldRProfessional title=F (chief physician；Associate chief physician；Attending doctor；Resident；Nothing)
FieldRMentorship=F (post-doctor counselor；Doctoral advisor；Master supervisor；Nothing)
FieldRPost=F (section directors；Section office deputy director；Chief resident doctor；Nothing)
Country's X level: country-level, national two grades, three grades of country；
Portion of province X level: province departmental level, two grades of portion of province；
ExperREducational background=F (doctor；Master；Scholar；Nothing)
ExperRWork=this speciality professional work time+net cycle time * F (surgery；Internal medicine；Training；Auxiliary departments)
ExperREducation=education time * F (speciality section office；Speciality associated department；Non-speciality associated department)
To each Authentication target, give weight according to degree of correlation, after summation, be the speciality recommendation degree of this expert:
Further, in step S4, patient includes, to expert opinion data, the patient satisfaction that place medical institutions collect
Evaluate, the patient of each big website evaluates, complains situation, dispute situation, malpractice situation.
Further, in step S5, hospital's data include hospital be subordinate to, Hospital Grade, hospital's attribute, hospital's qualification, section
Room qualification.
Further, in step S7, patient information includes basic document, case-data and demand for services.
A kind of medical expert's personalized recommendation system solved based on big data, including Medical Technologist data base, described doctor
Treat expert database and include that expert personal information data base, patient, to expert opinion document data base, hospital's document data base, go back
Including expert's personal information authentication module, expert's speciality recommendation degree computing module, patient to expert opinion data test module, doctor
Institute's data test module, expert's speciality recommended models memory module, patient information acquisition module, individual demand model storage mould
Block, personalized recommendation module；
Described expert personal information data base, expert's personal information authentication module, expert's speciality recommendation degree computing module depend on
Secondary connection；
Expert opinion data test module is connected by described patient by expert opinion document data base with described patient；
Described hospital document data base is connected with described hospital data test module；
Described expert's speciality recommended models memory module connects described expert's speciality recommendation degree computing module, patient couple respectively
Expert opinion data test module, hospital's data test module；
Described patient information acquisition module is connected with described individual demand model memory module；
Described personalized recommendation module connects described expert's speciality recommended models memory module, individual demand model respectively
Memory module；
Described expert personal information data base is used for storing expert's personal information；
Expert opinion document data base is used for storing patient to expert opinion data by described patient；
Described hospital document data base is used for storing hospital's data；
Described expert's personal information authentication module is for by big data method multi-party verification expert's personal information；
Described expert's speciality recommendation degree computing module is for calculating the speciality recommendation degree of expert；
Expert opinion data test module is used for being provided expert opinion by big data method checking patient by described patient
Material；
Described hospital data test module is for verifying hospital's data by big data method；
Expert's speciality that described expert's speciality recommended models memory module is set up according to three aspect comprehensive modelings for storage
Recommended models；
Described patient information acquisition module is used for obtaining patient information；
The individual demand model that described individual demand model memory module is set up according to patient information for storage；
Described personalized recommendation module is for carrying out personalization push away according to expert's speciality recommended models, individual demand model
Recommend medical expert.
Further, expert's personal information collection module, personal information modification and perfection module, described expert individual are also included
Collection of data module, personal information modification and perfection module, expert personal information data base are sequentially connected with；
Described expert's personal information collection module is for collecting expert's personal information by portal website of each hospital；
Described personal information modification and perfection module is modification and perfection personal information after expert's certification；
Also include patient expert opinion document data base being connected with described patient to expert opinion data acquisition module,
Expert opinion data acquisition module is used for obtaining patient to expert opinion data by third party website by described patient；
Also include the hospital's data acquisition module being connected with described hospital document data base, described hospital data acquisition module
For obtaining hospital's data by Hospital Website.
The medical expert's personalized recommendation method solved based on big data of the present invention and system, by big data method,
Set up a set of technology and algorithm, from the individual needs of professional speciality, service quality and the patient of expert, set up personalized expert and push away
Recommend, solve doctors and patients' information asymmetry contradiction, improve patient and seek medical advice precision, an abundant Appropriate application medical resource, it is achieved seek medical advice
Property, improve clinical efficacy.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, in embodiment being described below required for make
Accompanying drawing be briefly described, it should be apparent that, below describe in accompanying drawing be only some embodiments of the present invention, for
From the point of view of those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain other according to these accompanying drawings
Accompanying drawing.
Fig. 1 is the flow chart of medical expert's personalized recommendation method that the present invention solves based on big data；
Fig. 2 is the topological diagram of medical expert's personalized recommendation method that the present invention solves based on big data；
Fig. 3 is the structural representation of medical expert's personalized recommendation system embodiment that the present invention solves based on big data；
Fig. 4 is the structural representation of medical expert's another embodiment of personalized recommendation system that the present invention solves based on big data
Figure.
Detailed description of the invention
Understandable for enabling the above-mentioned purpose of the present invention, feature and advantage to become apparent from, below in conjunction with accompanying drawing with concrete
Embodiment technical scheme is described in detail.It is pointed out that described embodiment is only this
Bright a part of embodiment rather than whole embodiments, based on the embodiment in the present invention, those of ordinary skill in the art are not having
Have and make the every other embodiment obtained under creative work premise, broadly fall into the scope of protection of the invention.
Embodiment 1
As shown in Figure 1 and Figure 2, a kind of medical expert's personalized recommendation method solved based on big data, including walking as follows
Rapid:
S1, setting up Medical Technologist data base, Medical Technologist data base includes that expert's personal information, patient are to expert opinion
Data, hospital's data；
Specifically include following steps:
S11, by portal website of each hospital collect expert's personal information；
Modification and perfection personal information after S12, expert's certification；
S13, by third party website obtain patient to expert opinion data；
S14, obtain hospital data by Hospital Website；
Expert's personal information includes expert's basic document, educational background, degree, academic title, professional title, post, Dao Shizi
Lattice, speciality, education experience, work experience, deliver treatise, obtain problem, achievement, award, academic tenure；
S2, by big data method multi-party verification expert's personal information；
Multi-party verification includes constituent parts website authentication admitted patient's information, respectively association's website authentication science tenure information, base
Gold website authentication topic information, domestic and foreign databases checking treatise information, defend planning commission's website authentication certification information, learn letter net test
Card educational background degree；
By big data method, expert's speciality being carried out dynamic examining certification, authentication condition includes: this speciality work validity
(accept this speciality patient number/accept patient populations for medical treatment by the moon moon for medical treatment；Accept this speciality patient the moon for medical treatment and cure the number/moon this speciality healing patient
Number), this speciality industry degree of recognition (academic title, post, problem, achievement, treatise, award, academic tenure, mentorship), this speciality
Experience degree (this specialty educational background, this professional work time, training further study time, instruction of papil situation)；
S3, the speciality recommendation degree of calculating expert；
Specifically include following steps:
Note:
FieldRAcademic title=F (professor；Associate professor；Lecturer；Assiatant；Nothing)
FieldRProfessional title=F (chief physician；Associate chief physician；Attending doctor；Resident；Nothing)
FieldRMentorship=F (post-doctor counselor；Doctoral advisor；Master supervisor；Nothing)
FieldRPost=F (section directors；Section office deputy director；Chief resident doctor；Nothing)
Country's X level: country-level, national two grades, three grades of country；
Portion of province X level: save departmental level, two grades of portion of province (containing army's prize of the achievement)；
ExperREducational background=F (doctor；Master；Scholar；Nothing)
ExperRWork=this speciality professional work time+net cycle time * F (surgery；Internal medicine；Training；Auxiliary departments)
ExperREducation=education time * F (speciality section office；Speciality associated department；Non-speciality associated department)
To each Authentication target, give weight according to degree of correlation, after summation, be the speciality recommendation degree of this expert:
S4, by big data method checking patient to expert opinion data；
By big data method, the service satisfaction of expert is carried out dynamic evaluation, including: place medical institutions collect
Patient satisfaction is evaluated, the patient of each big website (good doctor, 999.com.cn, all kinds of APP etc.) evaluates, complain situation, dispute
Situation, malpractice situation etc.；
S5, by big data method checking hospital data；
By big data method, hospital and section office are carried out dynamic evaluation, including: hospital is subordinate to (public, private), hospital
Grade (one, two, three；First, second, the third gradegrade C), hospital's attribute (university teaching hospital, general hospital, section hospital, community hospital
Deng), hospital's qualification (whole nation hundred good, national rankings etc.), (state key subject, provinces and cities' key discipline, country are clinical for section office's qualification
Pivot Subjects, provinces and cities' Pivot Subjects, degree point, specific technique training base etc.)；
S6, according to above-mentioned three aspect comprehensive modelings, set up expert's speciality recommended models；
According to above-mentioned three aspect comprehensive modelings, speciality a certain to expert carries out real time comprehensive grading and (monthly updates and once comment
Valency result)；
S7, acquisition patient information；
Patient information includes basic document, case-data and demand for services, and patient submits part individual's symptom to, checks, examines
Test result (or diagnosis), demand data；
S8, set up individual demand model according to patient information；
S9, carry out personalized recommendation medical expert according to expert's speciality recommended models, individual demand model；
Set up individual demand model according to patient data, pushed away by patient demand model Auto-matching expert's speciality
Recommend, recommendation considers hospital's geographical location information, from the close-by examples to those far off recommends.
Embodiment 2
As it is shown on figure 3, a kind of medical expert's personalized recommendation system solved based on big data, including Medical Technologist's data
Storehouse, described Medical Technologist data base includes that expert personal information data base, patient are to expert opinion document data base, hospital's data
Data base, also includes that expert opinion data is tested by expert's personal information authentication module, expert's speciality recommendation degree computing module, patient
Card module, hospital's data test module, expert's speciality recommended models memory module, patient information acquisition module, individual demand
Model memory module, personalized recommendation module；
Described expert personal information data base, expert's personal information authentication module, expert's speciality recommendation degree computing module depend on
Secondary connection；
Expert opinion data test module is connected by described patient by expert opinion document data base with described patient；
Described hospital document data base is connected with described hospital data test module；
Described expert's speciality recommended models memory module connects described expert's speciality recommendation degree computing module, patient couple respectively
Expert opinion data test module, hospital's data test module；
Described patient information acquisition module is connected with described individual demand model memory module；
Described personalized recommendation module connects described expert's speciality recommended models memory module, individual demand model respectively
Memory module；
Described expert personal information data base is used for storing expert's personal information；
Expert opinion document data base is used for storing patient to expert opinion data by described patient；
Described hospital document data base is used for storing hospital's data；
Described expert's personal information authentication module is for by big data method multi-party verification expert's personal information；
Described expert's speciality recommendation degree computing module is for calculating the speciality recommendation degree of expert；
Expert opinion data test module is used for being provided expert opinion by big data method checking patient by described patient
Material；
Described hospital data test module is for verifying hospital's data by big data method；
Expert's speciality that described expert's speciality recommended models memory module is set up according to three aspect comprehensive modelings for storage
Recommended models；
Described patient information acquisition module is used for obtaining patient information；
The individual demand model that described individual demand model memory module is set up according to patient information for storage；
Described personalized recommendation module is for carrying out personalization push away according to expert's speciality recommended models, individual demand model
Recommend medical expert.
Embodiment 3
As shown in Figure 4, a kind of medical expert's personalized recommendation system solved based on big data, including Medical Technologist's data
Storehouse, described Medical Technologist data base includes that expert personal information data base, patient are to expert opinion document data base, hospital's data
Data base, also includes that expert opinion data is tested by expert's personal information authentication module, expert's speciality recommendation degree computing module, patient
Card module, hospital's data test module, expert's speciality recommended models memory module, patient information acquisition module, individual demand
Model memory module, personalized recommendation module；
Described expert personal information data base, expert's personal information authentication module, expert's speciality recommendation degree computing module depend on
Secondary connection；
Expert opinion data test module is connected by described patient by expert opinion document data base with described patient；
Described hospital document data base is connected with described hospital data test module；
Described expert's speciality recommended models memory module connects described expert's speciality recommendation degree computing module, patient couple respectively
Expert opinion data test module, hospital's data test module；
Described patient information acquisition module is connected with described individual demand model memory module；
Described personalized recommendation module connects described expert's speciality recommended models memory module, individual demand model respectively
Memory module；
Described expert personal information data base is used for storing expert's personal information；
Expert opinion document data base is used for storing patient to expert opinion data by described patient；
Described hospital document data base is used for storing hospital's data；
Described expert's personal information authentication module is for by big data method multi-party verification expert's personal information；
Described expert's speciality recommendation degree computing module is for calculating the speciality recommendation degree of expert；
Expert opinion data test module is used for being provided expert opinion by big data method checking patient by described patient
Material；
Described hospital data test module is for verifying hospital's data by big data method；
Expert's speciality that described expert's speciality recommended models memory module is set up according to three aspect comprehensive modelings for storage
Recommended models；
Described patient information acquisition module is used for obtaining patient information；
The individual demand model that described individual demand model memory module is set up according to patient information for storage；
Described personalized recommendation module is for carrying out personalization push away according to expert's speciality recommended models, individual demand model
Recommend medical expert；
Also including expert's personal information collection module, personal information modification and perfection module, described expert's personal information is collected
Module, personal information modification and perfection module, expert personal information data base are sequentially connected with；
Described expert's personal information collection module is for collecting expert's personal information by portal website of each hospital；
Described personal information modification and perfection module is modification and perfection personal information after expert's certification；
Also include patient expert opinion document data base being connected with described patient to expert opinion data acquisition module,
Expert opinion data acquisition module is used for obtaining patient to expert opinion data by third party website by described patient；
Also include the hospital's data acquisition module being connected with described hospital document data base, described hospital data acquisition module
For obtaining hospital's data by Hospital Website.
The medical expert's personalized recommendation method solved based on big data of the present invention and system, by big data method,
Set up a set of technology and algorithm, from the individual needs of professional speciality, service quality and the patient of expert, set up personalized expert and push away
Recommend, solve doctors and patients' information asymmetry contradiction, improve patient and seek medical advice precision, an abundant Appropriate application medical resource, it is achieved seek medical advice
Property, improve clinical efficacy.
Embodiment described above only have expressed the several embodiments of the present invention, and it describes more concrete and detailed, but also
Therefore the restriction to the scope of the claims of the present invention can not be interpreted as.It should be pointed out that, for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, it is also possible to make some deformation and improvement, these broadly fall into the guarantor of the present invention
Protect scope.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. the medical expert's personalized recommendation method solved based on big data, it is characterised in that comprise the steps:
S1, setting up Medical Technologist data base, Medical Technologist data base includes that expert opinion is provided by expert's personal information, patient
Material, hospital's data；
S2, by big data method multi-party verification expert's personal information；
S3, the speciality recommendation degree of calculating expert；
S4, by big data method checking patient to expert opinion data；
S5, by big data method checking hospital data；
S6, according to above-mentioned three aspect comprehensive modelings, set up expert's speciality recommended models；
S7, acquisition patient information；
S8, set up individual demand model according to patient information；
S9, carry out personalized recommendation medical expert according to expert's speciality recommended models, individual demand model.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
Rapid S1 specifically includes following steps:
S11, by portal website of each hospital collect expert's personal information；
Modification and perfection personal information after S12, expert's certification；
S13, by third party website obtain patient to expert opinion data；
S14, obtain hospital data by Hospital Website.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
In rapid S1, expert's personal information includes expert's basic document, educational background, degree, academic title, professional title, post, Dao Shizi
Lattice, speciality, education experience, work experience, deliver treatise, obtain problem, achievement, award, academic tenure.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
In rapid S2, multi-party verification includes constituent parts website authentication admitted patient's information, respectively association's website authentication science tenure information, fund
Website authentication topic information, domestic and foreign databases are verified treatise information, are defended planning commission's website authentication certification information, letter net checking
Educational background degree.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
Rapid S3 specifically includes following steps:
Note:
FieldRAcademic title=F (professor；Associate professor；Lecturer；Assiatant；Nothing)
FieldRProfessional title=F (chief physician；Associate chief physician；Attending doctor；Resident；Nothing)
FieldRMentorship=F (post-doctor counselor；Doctoral advisor；Master supervisor；Nothing)
FieldRPost=F (section directors；Section office deputy director；Chief resident doctor；Nothing)
Country's X level: country-level, national two grades, three grades of country；
Portion of province X level: province departmental level, two grades of portion of province；
ExperREducational background=F (doctor；Master；Scholar；Nothing)
ExperRWork=this speciality professional work time+net cycle time * F (surgery；Internal medicine；Training；Auxiliary departments)
ExperREducation=education time * F (speciality section office；Speciality associated department；Non-speciality associated department)
To each Authentication target, give weight according to degree of correlation, after summation, be the speciality recommendation degree of this expert:
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
In rapid S4, patient includes patient satisfaction evaluation, the patient of each big website that place medical institutions collect to expert opinion data
Evaluate, complain situation, dispute situation, malpractice situation.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
In rapid S5, hospital's data include hospital be subordinate to, Hospital Grade, hospital's attribute, hospital's qualification, section office's qualification.
The medical expert's personalized recommendation method solved based on big data the most according to claim 1, it is characterised in that step
In rapid S7, patient information includes basic document, case-data and demand for services.
9. the medical expert's personalized recommendation system solved based on big data, it is characterised in that include Medical Technologist's data
Storehouse, described Medical Technologist data base includes that expert personal information data base, patient are to expert opinion document data base, hospital's data
Data base, also includes that expert opinion data is tested by expert's personal information authentication module, expert's speciality recommendation degree computing module, patient
Card module, hospital's data test module, expert's speciality recommended models memory module, patient information acquisition module, individual demand
Model memory module, personalized recommendation module；
Described expert personal information data base, expert's personal information authentication module, expert's speciality recommendation degree computing module connect successively
Connect；
Expert opinion data test module is connected by described patient by expert opinion document data base with described patient；
Described hospital document data base is connected with described hospital data test module；
Described expert's speciality recommended models memory module connects described expert's speciality recommendation degree computing module, patient respectively to expert
Evaluate data test module, hospital's data test module；
Described patient information acquisition module is connected with described individual demand model memory module；
Described personalized recommendation module connects described expert's speciality recommended models memory module, the storage of individual demand model respectively
Module；
Described expert personal information data base is used for storing expert's personal information；
Expert opinion document data base is used for storing patient to expert opinion data by described patient；
Described hospital document data base is used for storing hospital's data；
Described expert's personal information authentication module is for by big data method multi-party verification expert's personal information；
Described expert's speciality recommendation degree computing module is for calculating the speciality recommendation degree of expert；
Expert opinion data test module is used for by big data method checking patient expert opinion data by described patient；
Described hospital data test module is for verifying hospital's data by big data method；
Expert's speciality that described expert's speciality recommended models memory module is set up according to three aspect comprehensive modelings for storage is recommended
Model；
Described patient information acquisition module is used for obtaining patient information；
The individual demand model that described individual demand model memory module is set up according to patient information for storage；
Described personalized recommendation module is for carrying out personalized recommendation doctor according to expert's speciality recommended models, individual demand model
Learn expert.
The medical expert's personalized recommendation system solved based on big data the most according to claim 9, it is characterised in that
Also include expert's personal information collection module, personal information modification and perfection module, described expert's personal information collection module, individual
Data modification improves module, expert personal information data base is sequentially connected with；
Described expert's personal information collection module is for collecting expert's personal information by portal website of each hospital；
Described personal information modification and perfection module is modification and perfection personal information after expert's certification；
Also include patient expert opinion document data base being connected with described patient to expert opinion data acquisition module, described
Expert opinion data acquisition module is used for obtaining patient to expert opinion data by third party website by patient；
Also including the hospital's data acquisition module being connected with described hospital document data base, described hospital data acquisition module is used for
Hospital's data is obtained by Hospital Website.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909389A (en) * | 2017-10-31 | 2018-04-13 | 深圳市宇数科技有限公司 | A kind of Data Mining consultation method, system, electronic equipment and storage medium |
| CN108039200A (en) * | 2017-12-22 | 2018-05-15 | 东软集团股份有限公司 | A kind of information recommendation method, device and storage medium, program product |
| CN108962359A (en) * | 2018-06-29 | 2018-12-07 | 北京百度网讯科技有限公司 | Medical treatment scheme determines method, device and equipment |
| CN109448807A (en) * | 2018-10-12 | 2019-03-08 | 成都数联易康科技有限公司 | A kind of patient's medical treatment guiding implementation method based on doctor's medical services behavioural analysis |
| CN110909236A (en) * | 2019-10-24 | 2020-03-24 | 东莞成电智信信息科技有限公司 | An expert recommendation method based on big data |
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| CN111951938A (en) * | 2020-07-06 | 2020-11-17 | 广州市家庭医生在线信息有限公司 | A hospital warehouse management system |
| CN113140303A (en) * | 2020-12-31 | 2021-07-20 | 上海明品医学数据科技有限公司 | Medical information answering system and method |
| CN115238168A (en) * | 2022-06-02 | 2022-10-25 | 郑州大学 | Self-adaptive remote medical expert recommendation method |
| CN117524434A (en) * | 2023-11-17 | 2024-02-06 | 中国人民解放军海军第九七一医院 | Expert information management optimization method and system based on intravenous treatment data platform |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102156706A (en) * | 2011-01-28 | 2011-08-17 | 清华大学 | Mentor recommendation system and method |
| CN102479202A (en) * | 2010-11-26 | 2012-05-30 | 卓望数码技术（深圳）有限公司 | Recommendation system based on domain expert |
| CN102855241A (en) * | 2011-06-28 | 2013-01-02 | 上海迈辉信息技术有限公司 | Multi-index expert suggestion system and realization method thereof |
| CN102880657A (en) * | 2012-08-31 | 2013-01-16 | 电子科技大学 | Searcher-Based Expert Recommendation Method |
| CN103559637A (en) * | 2013-11-13 | 2014-02-05 | 王竞 | Method and system for recommending doctor for patient |
| CN104361102A (en) * | 2014-11-24 | 2015-02-18 | 清华大学 | Expert recommendation method and system based on group matching |
-
2016
- 2016-07-29 CN CN201610624958.7A patent/CN106295147A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102479202A (en) * | 2010-11-26 | 2012-05-30 | 卓望数码技术（深圳）有限公司 | Recommendation system based on domain expert |
| CN102156706A (en) * | 2011-01-28 | 2011-08-17 | 清华大学 | Mentor recommendation system and method |
| CN102855241A (en) * | 2011-06-28 | 2013-01-02 | 上海迈辉信息技术有限公司 | Multi-index expert suggestion system and realization method thereof |
| CN102880657A (en) * | 2012-08-31 | 2013-01-16 | 电子科技大学 | Searcher-Based Expert Recommendation Method |
| CN103559637A (en) * | 2013-11-13 | 2014-02-05 | 王竞 | Method and system for recommending doctor for patient |
| CN104361102A (en) * | 2014-11-24 | 2015-02-18 | 清华大学 | Expert recommendation method and system based on group matching |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909389A (en) * | 2017-10-31 | 2018-04-13 | 深圳市宇数科技有限公司 | A kind of Data Mining consultation method, system, electronic equipment and storage medium |
| CN107909389B (en) * | 2017-10-31 | 2020-03-17 | 深圳市宇数科技有限公司 | Data exploration and consultation method and system, electronic equipment and storage medium |
| CN108039200A (en) * | 2017-12-22 | 2018-05-15 | 东软集团股份有限公司 | A kind of information recommendation method, device and storage medium, program product |
| CN108962359A (en) * | 2018-06-29 | 2018-12-07 | 北京百度网讯科技有限公司 | Medical treatment scheme determines method, device and equipment |
| CN109448807A (en) * | 2018-10-12 | 2019-03-08 | 成都数联易康科技有限公司 | A kind of patient's medical treatment guiding implementation method based on doctor's medical services behavioural analysis |
| CN110909236A (en) * | 2019-10-24 | 2020-03-24 | 东莞成电智信信息科技有限公司 | An expert recommendation method based on big data |
| CN111143668A (en) * | 2019-12-06 | 2020-05-12 | 广州市医康传媒信息技术有限公司 | Medical resource recommendation information processing system, method, device and storage medium |
| CN111354451A (en) * | 2020-03-04 | 2020-06-30 | 中国医科大学附属盛京医院 | Intelligent recommendation method and system for hospital nursing service personnel |
| CN111951938A (en) * | 2020-07-06 | 2020-11-17 | 广州市家庭医生在线信息有限公司 | A hospital warehouse management system |
| CN113140303A (en) * | 2020-12-31 | 2021-07-20 | 上海明品医学数据科技有限公司 | Medical information answering system and method |
| CN115238168A (en) * | 2022-06-02 | 2022-10-25 | 郑州大学 | Self-adaptive remote medical expert recommendation method |
| CN117524434A (en) * | 2023-11-17 | 2024-02-06 | 中国人民解放军海军第九七一医院 | Expert information management optimization method and system based on intravenous treatment data platform |
| CN117524434B (en) * | 2023-11-17 | 2024-04-30 | 中国人民解放军海军第九七一医院 | Expert information management optimization method and system based on vein treatment data platform |
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