CN111554387B - Doctor information recommendation method and device, storage medium and electronic equipment - Google Patents
Doctor information recommendation method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN111554387B CN111554387B CN202010338556.7A CN202010338556A CN111554387B CN 111554387 B CN111554387 B CN 111554387B CN 202010338556 A CN202010338556 A CN 202010338556A CN 111554387 B CN111554387 B CN 111554387B
- Authority
- CN
- China
- Prior art keywords
- doctor
- disease
- time period
- sub
- coefficient
- 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.)
- Active
Links
Images
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
- 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
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The embodiment of the disclosure provides a doctor information recommendation method, a device, a storage medium and electronic equipment, and relates to the technical fields of computer technology and information processing. The method comprises the following steps: determining a correspondence of a doctor to at least one disease based on medical record data within a preset time period; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient. The method and the device realize that the doctor recommendation information is determined based on the weight coefficient and the punishment coefficient, wherein the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the punishment coefficient is determined based on the types of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the types of diagnosing the disease by the doctor are comprehensively considered during the doctor recommendation, the automatic recommendation of the doctor can be realized, the recommendation efficiency of the doctor is improved, and the recommendation accuracy of the doctor is improved.
Description
Technical Field
The disclosure relates to the field of computer technology and information processing technology, in particular to a method, a device, a storage medium and electronic equipment for recommending doctor information.
Background
Today, with electronic informatization, doctor recommendation is an important function in an internet hospital in an area.
In the related art, doctor recommendation is generally performed in the following manner: one is a manual recommendation, such as a public praise recommendation by a doctor. The other is according to the doctor's receptionist recommendation.
In the above technique, all diseases and doctors cannot be covered by manual recommendation, and standards and results recommended by different people are inconsistent. According to the doctor's receptivity recommendation, there is a doctor who is not necessarily the most specialized for the specific disease, and many new specialists are available each year, and new technologies do not consider the time impact on recommendation.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, a computer readable medium and electronic equipment for recommending doctor information, so as to improve flexibility and accuracy of recommending doctor information at least to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to an aspect of the embodiments of the present disclosure, there is provided a method for recommending doctor information, including: determining a correspondence of a doctor to at least one disease based on medical record data within a preset time period; determining a weight coefficient and a penalty coefficient of the doctor for the first disease based on the correspondence; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient.
In some exemplary embodiments of the disclosure, based on the foregoing approach, determining the weight coefficient of the doctor for the first disease based on the correspondence includes: determining a plurality of sub-time periods included in the preset time period according to the preset time period; determining the times that the doctor diagnoses the first disease in each sub-time period in the preset time period according to the corresponding relation; and determining a weight coefficient of the doctor for the first disease according to the times of diagnosis of the first disease by the doctor in each sub-time period.
In some exemplary embodiments of the present disclosure, based on the foregoing scheme, determining the weight coefficient of the doctor for the first disease according to the number of times the doctor has diagnosed the first disease in each sub-period of time includes: determining a sub-weight coefficient of the doctor for the disease in each sub-time period according to the times that the doctor diagnoses the first disease in each sub-time period; determining a weight coefficient of the doctor for the first disease according to a sub weight coefficient of the doctor for the first disease in each sub time period.
In some exemplary embodiments of the disclosure, based on the foregoing approach, determining a penalty factor for the doctor for the first disease based on the correspondence includes: determining a plurality of sub-time periods included in the preset time period according to the preset time period; based on the corresponding relation, determining the number of diseases corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period; and determining the punishment coefficient of the doctor based on the number of diseases corresponding to the doctor in the preset time period and the sub punishment coefficient of the doctor in each sub time period in the preset time period.
In some exemplary embodiments of the present disclosure, based on the foregoing solution, determining, based on the correspondence, the number of diseases corresponding to the doctor in the preset time period and a sub-penalty coefficient of the doctor in each sub-time period in the preset time period includes: and determining a sub-punishment coefficient of each doctor in each sub-time period based on the disease duplication removal number corresponding to each doctor in each sub-time period in the appointed preset time period.
In some exemplary embodiments of the disclosure, based on the foregoing scheme, the method further includes: determining recommended information of each doctor for the first disease based on medical record data in the preset time period; and arranging the recommended information of each doctor aiming at the first disease according to a preset sequence, and displaying the recommended information to the patient so as to provide the doctor aiming at the first disease for the patient.
In some exemplary embodiments of the present disclosure, based on the foregoing aspects, determining a correspondence of a doctor to at least one disease based on medical record data over a preset period of time includes: acquiring hospitalization medical records from case data in a preset time period; and extracting the doctor and at least one disease diagnosed by the doctor from the hospitalized medical records.
According to an aspect of the embodiments of the present disclosure, there is provided an apparatus for recommending doctor information, including: the data input module is configured to determine the corresponding relation between doctors and at least one disease based on medical record data in a preset time period; a coefficient determination module configured to determine a weight coefficient and a penalty coefficient of the doctor for a first disease based on the correspondence; wherein the at least one disease comprises the first disease; an information determination module configured to determine recommended information of the physician for the first disease based on the weight coefficient and the penalty coefficient.
In some exemplary embodiments of the disclosure, based on the foregoing, the coefficient determining module includes: a first determining unit configured to determine a plurality of sub-time periods included in the preset time period according to the preset time period; a second determining unit configured to determine, according to the correspondence, the number of times the doctor has diagnosed the first disease in each sub-period of time within the preset period of time; and a third determination unit configured to determine a weight coefficient of the doctor for the first disease according to the number of times the doctor diagnoses the first disease in each sub-period.
In some exemplary embodiments of the present disclosure, based on the foregoing, the third determining unit is configured to determine a sub-weight coefficient of the doctor for the disease in each sub-period according to the number of times the doctor has diagnosed the first disease in each sub-period; determining a weight coefficient of the doctor for the first disease according to a sub weight coefficient of the doctor for the first disease in each sub time period.
In some exemplary embodiments of the disclosure, based on the foregoing aspect, the coefficient determining module further includes: a fourth determining unit configured to determine a plurality of sub-time periods included in the preset time period according to the preset time period; a fifth determining unit configured to determine, based on the correspondence, the number of diseases corresponding to the doctor in the preset time period and a sub-penalty coefficient of the doctor in each sub-time period in the preset time period; and a sixth determining unit configured to determine a punishment coefficient of the doctor based on the number of diseases corresponding to the doctor in the preset time period and a sub-punishment coefficient of the doctor in each sub-time period in the preset time period.
In some exemplary embodiments of the present disclosure, based on the foregoing solution, the fifth determining unit is configured to determine the sub-penalty coefficient of the doctor in each sub-period based on the number of disease deduplication corresponding to the doctor in each sub-period in the specified preset period.
In some exemplary embodiments of the disclosure, based on the foregoing, the apparatus further includes: a doctor recommendation module configured to determine recommendation information of each doctor for the first disease based on medical record data within the preset time period; and arranging the recommended information of each doctor aiming at the first disease according to a preset sequence, and displaying the recommended information to the patient so as to provide the doctor aiming at the first disease for the patient.
In some exemplary embodiments of the disclosure, based on the foregoing, the data input module is configured to obtain an inpatient medical record from case data within a preset period of time; and extracting the doctor and at least one disease diagnosed by the doctor from the hospitalized medical records.
According to an aspect of the disclosed embodiments, there is provided a computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method as described in the above embodiments.
According to an aspect of an embodiment of the present disclosure, there is provided an electronic device including: one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method as described in the above embodiments.
In the embodiment of the invention, the corresponding relation between doctors and at least one disease is determined based on medical record data in a preset time period; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient. The method and the device realize that the doctor recommendation information is determined based on the weight coefficient and the punishment coefficient, wherein the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the punishment coefficient is determined based on the types of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the types of diagnosing the disease by the doctor are comprehensively considered during the doctor recommendation, the automatic recommendation of the doctor can be realized, the recommendation efficiency of the doctor is improved, and the recommendation accuracy of the doctor is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
In the drawings:
FIG. 1 illustrates a schematic diagram of an exemplary system architecture 100 to which methods or apparatus of physician information recommendation of embodiments of the present disclosure may be applied;
FIG. 2 schematically illustrates a flow chart of a method of physician information recommendation according to one embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of physician information recommendation according to another embodiment of the present disclosure;
FIG. 4 schematically illustrates a block diagram of an apparatus for physician information recommendation, according to an embodiment of the present disclosure;
fig. 5 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 illustrates a schematic diagram of an exemplary system architecture 100 to which methods or apparatus of physician information recommendation of embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. The terminal devices 101, 102, 103 may be various electronic devices with display screens including, but not limited to, smartphones, tablet computers, portable computers, desktop computers, and the like.
The server 105 may be a server providing various services. For example, the terminal device 103 (may also be the terminal device 101 or 102) sends a doctor recommendation request for the first disease to the server 105, and the server 105 may determine a correspondence between a doctor and at least one disease based on medical record data within a preset period of time; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining the recommendation information of the doctor for the first disease based on the weight coefficient and the punishment coefficient, integrating the recommendation information of each doctor for the first disease, determining the recommendation information of the doctor for the first disease from the recommendation information of each doctor for the first disease, and sending the recommendation information of the doctor to the terminal 103, wherein the terminal 103 can display the recommendation information of the doctor.
Fig. 2 schematically illustrates a flow chart of a method of physician information recommendation according to one embodiment of the present disclosure. The method provided in the embodiments of the present disclosure may be processed by any electronic device having computing processing capability, for example, the server 105 and/or the terminal devices 102 and 103 in the embodiment of fig. 1, and in the following embodiments, the server 105 is taken as an example to illustrate the execution subject, but the present disclosure is not limited thereto.
As shown in fig. 2, the method for recommending doctor information provided by the embodiment of the disclosure may include the following steps:
in step S210, a correspondence between a doctor and at least one disease is determined based on medical record data within a preset period of time.
In the embodiment of the disclosure, the doctor information recommendation may be performed on a target area, where the target area may be a hospital, a city, a country, or the like. For example, if the correspondence between the doctor and the disease is determined based on the medical record data of Beijing city in the preset time period, the doctor recommendation information of Beijing city can be determined.
It should be noted that, in the correspondence between a doctor and at least one disease determined in the embodiment of the present invention, the doctor may be any one of all doctors included in medical record data within a preset period of time, and the recommended information of each doctor for the first disease may be determined by a polling method.
It should be noted that, when recording the correspondence between a doctor and at least one disease, information of the doctor, such as doctor name, hospital, work experience, obtained achievement, contact information, and disease stage of good quality, etc., may be recorded.
In the embodiment of the disclosure, case data (including electronic medical record data and handwritten medical record data) in a preset time period can be acquired from a hospital in a target area, an inpatient medical record is acquired from the case data, and the inpatient medical record and at least one disease diagnosed by the doctor are extracted.
In the embodiment of the present invention, the medical records in hospital may be classified into discharge diagnosis and admission diagnosis, and when at least one of the above-mentioned doctor and the disease diagnosed by the doctor is extracted from the medical records in hospital, all records of the attending doctor as the above-mentioned doctor may be extracted from the doctor discharge diagnosis, the disease suffered by the patient may be extracted from the admission diagnosis corresponding to each discharge diagnosis of the above-mentioned doctor by the attending doctor, and the disease may be normalized according to the 10 th revision (abbreviated as ICD-10) of the international disease classification (international Classification of diseases, ICD), and the disease (at least one) corresponding to each admission diagnosis may be extracted. Thereby extracting the doctor and at least one disease diagnosed by the doctor.
It should be noted that other criteria may be used in normalizing the disease of the patient in the admission diagnosis. If the disease and the doctor are already included in the admission diagnosis or discharge diagnosis, the doctor and the disease diagnosed by the doctor may be directly extracted only by means of the admission diagnosis or discharge diagnosis.
It should be noted that each hospitalized medical record can extract at least one doctor and the disease diagnosed by that doctor.
In the embodiment of the present invention, the preset time period may refer to the last years, for example, 5 years, from the current time (or a certain preset time), and the preset time period refers to 2015 to 2020 assuming that the current time is 2020. The medical record data extraction method based on the medical record data in the preset time period can extract the corresponding relation between the doctor and at least one disease which is mainly treated by the doctor from medical record data in hospitals from a certain area in 2015 to 2020.
In step S220, a weight coefficient and a penalty coefficient of the doctor for a first disease are determined based on the correspondence, wherein the at least one disease comprises the first disease.
It should be noted that the first disease may be any one of at least one disease corresponding to the doctor.
In the embodiment of the invention, a plurality of sub-time periods included in a preset time period can be determined according to the preset time period. For example, when the preset time period is the first 5 years from the current (or a specific time), and the current time is 2 days of 3 months in 2020, the 5 years may be divided into 5 sub-time periods, which are respectively:
the specific time corresponding to the 1 st year from the current time is 3 months from 3 days in 2019 to 3 months and 2 days in 2020; the specific time corresponding to the 2 nd year from the current time is 3 rd month of 2018 to 3 rd month 2 nd day of 2019; the 3 rd year from the current time corresponds to the specific time from 3 rd month of 2017 to 2 rd month of 2018; the specific time corresponding to the 4 th year from the current time is 2016 3 months 3 days to 2017 3 months 2 days; and the 5 th year from the current time, the corresponding specific time is from 3 months of 2015 to 3 months of 2016.
In the embodiment of the present invention, after determining a plurality of sub-time periods included in a preset time period, the number of times that the doctor diagnoses the first disease in each sub-time period in the preset time period may be determined based on the correspondence, and the weight coefficient of the doctor for the first disease may be determined according to the number of times that the doctor diagnoses the first disease in each sub-time period.
It should be noted that, the weight coefficient refers to the importance degree of a certain factor or index relative to a certain object, and in the embodiment of the present invention, the weight coefficient refers to the importance degree of the first disease relative to all diseases treated by the doctor, and if the weight coefficient of a doctor relative to the first disease is higher, it means that the doctor is more likely to recommend the higher the number of times of treating the first disease. The sub-weight coefficient refers to the importance of the first disease to all diseases treated by the physician during the sub-time period.
According to the embodiment of the invention, the sub-weight coefficient of the doctor for the first disease in each sub-time period can be determined according to the times that the doctor diagnoses the first disease in each sub-time period, and then the weight coefficient of the doctor for the first disease is determined according to the sub-weight coefficient of the doctor for the first disease in each sub-time period.
The embodiment of the invention provides a method for determining the weight coefficient of each doctor for each disease, taking the previous 5 years from the current time as an example of a preset time period, the weight coefficient of a doctor p for a first disease d can be determined by the following formula:
wherein DF (p, d) represents the weight coefficient of doctor p for the first disease d, N i,p,d Represents the number of times of the first disease d corresponding to the ith doctor p from the current time, and gamma represents the attenuation coefficient, which is a constant, such as 0.7, and can be obtained based on experimental data, wherein gamma i N i,p,d Representing the sub-weight coefficient of doctor p for the first disease d from the i-th year of the current time.
It should be noted that, as shown in the formula (1), the more times the doctor p treats the first disease d, the higher the weight coefficient thereof, and the easier the follow-up is recommended. In the embodiment of the invention, the more times of treating a certain disease, the more easily recommended rules of doctors are reflected in the calculation process of the weight coefficient, so that doctor recommendation based on the times of diagnosing the disease by the doctors is realized.
It should be noted that, as shown in the formula (1), as i increases, the longer the current time is, the smaller γ is 1, which corresponds to γ i Smaller and smaller, e.g., γ=0.7, when i=1, γ i =0.7, when i=2, γ i =0.49. According to the embodiment of the invention, the calculation of the doctor weight based on time is realized, the closer the doctor weight is to the current time, the higher the obtained doctor weight is, the weight coefficient of the recent doctor for diagnosing diseases is improved, the relation of the doctor capability changing along with time is reflected to the calculation process of the weight coefficient, the doctor recommendation based on time change is realized, and the reliability and the accuracy of the weight coefficient are improved.
According to an embodiment of the present invention, the weight coefficient of the doctor for the first disease may be determined based on the above formula (1).
According to the embodiment of the invention, a plurality of sub-time periods included in the preset time period can be determined according to the preset time period, after the plurality of sub-time periods included in the preset time period are determined, the number of diseases corresponding to the doctor in the preset time period and the sub-penalty coefficient of the doctor in each sub-time period in the preset time period can be determined based on the corresponding relation, and then the penalty coefficient of the doctor is determined based on the number of diseases corresponding to the doctor in the preset time period and the sub-penalty coefficient of the doctor in each sub-time period in the preset time period.
In the embodiment of the invention, the punishment coefficient and the sub-punishment coefficient are used for representing the repetition degree of the diseases corresponding to the doctor, and if the repeatability of the diseases corresponding to the doctor is higher, the sub-punishment coefficient is lower, the punishment coefficient is higher, which indicates that the doctor is a specialist doctor and the doctor is easier to recommend.
Note that each sub-period of the preset period may be the same as or different from each sub-period in determining the weight coefficient.
In the embodiment of the invention, after each sub-time period of a preset time period is determined, the sub-punishment coefficient of each doctor in each sub-time period can be determined based on the disease weight removal number corresponding to the doctor in each sub-time period in the preset time period, and then the punishment coefficient of the doctor is determined based on the disease number corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period.
The embodiment of the invention provides a method for determining the punishment coefficient of doctors, taking the first 5 years from the current time as an example, the punishment coefficient of each doctor p can be determined by the following formula:
wherein IDF (p) represents penalty factor of doctor p, D represents number of diseases corresponding to doctor p in preset time period, D i,p Represents the number of patients with the disease corresponding to the ith doctor p from the current time after the duplication removal, D i,p Less than or equal to D, gamma represents the attenuation coefficient, is a constant, such as 0.7, and can be obtained based on experimental data, wherein gamma i D i,p A sub-penalty coefficient representing the i-th physician p from the current time.
It should be noted that, as shown in the formula (2), the number of disease types corresponding to the doctor p in the preset time period is a certain value, which is the sum of the numbers of all disease types corresponding to the doctor p. When a doctor has higher repeatability of the corresponding disease types in a certain subperiod, the number of the residual diseases after the duplication removal is lower, the sub penalty coefficient in the subperiod is lower, the sum of the sub penalty coefficients is lower, and when the penalty coefficient is calculated later, the value of dividing D by the sum of the sub penalty coefficients is larger (note that D is larger than or equal to the sum of the sub penalty coefficients, if the repeatability of the disease types is higher, D is larger than the sum of the sub penalty coefficients), the penalty coefficient obtained by taking logarithm is higher, and when doctor recommendation information is determined, the value obtained by multiplying the penalty coefficient by the weight coefficient is higher, the doctor is more easily recommended. In the embodiment of the invention, considering that all diseases of primary outpatient or general practitioner can be seen, and the diseases corresponding to special practitioner are repeated, the more repeated the diseases corresponding to the practitioner are, the more easily recommended rule of the practitioner is reflected in the calculation process of punishment coefficient, and the recommendation of the practitioner based on special \general practitioner is realized.
According to an embodiment of the present invention, the penalty factor of the doctor may be determined based on the above formula (2). It should be noted that the penalty factor of the doctor is the same for each disease during the preset time period.
In S230, recommendation information for the doctor for the first disease is determined based on the weight coefficient and the penalty coefficient.
According to the embodiment of the invention, the recommended information of each doctor for the first disease, which is extracted from the case data, can be determined based on the case history data in the preset time period, and the recommended information of each doctor for the first disease is arranged according to the preset sequence and then displayed to the patient, so that the doctor for the first disease is provided for the patient.
It should be noted that, the recommended information of the doctor for the first disease may be a recommendation coefficient (recommendation degree value), and after the recommendation coefficient of each doctor for the first disease is obtained, each doctor may be arranged in order from high to low according to the recommendation coefficient, and displayed to the patient.
According to the embodiment of the invention, the recommendation coefficient of the doctor can be determined by multiplying the weight coefficient by the punishment coefficient. For example, using DF (p, d). Times.IDF (p) in the above formula, the doctor's p recommendation coefficient for the first disease d can be obtained. After determining the recommendation coefficients of the doctors for the first diseases, determining the recommendation information of the doctors for the first diseases according to the order of the recommendation coefficients of the diseases from high to low.
It should be noted that the displayed doctor may be a doctor who intercepts the part from high to low according to the recommendation coefficient of the first disease. The information of these doctors can be displayed in a preset or custom format. Doctor information may include, but is not limited to, doctor name, hospital, work history, achievement achieved, contact, and disease stage of a person skilled in the art.
In the embodiment of the invention, the corresponding relation between doctors and at least one disease is determined based on medical record data in a preset time period; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient. The method and the device realize that the doctor recommendation information is determined based on the weight coefficient and the punishment coefficient, wherein the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the punishment coefficient is determined based on the types of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the types of diagnosing the disease by the doctor are comprehensively considered during the doctor recommendation, the automatic recommendation of the doctor can be realized, the recommendation efficiency of the doctor is improved, and the recommendation accuracy of the doctor is improved.
In one embodiment of the invention, a rare disease list can be preset, a corresponding relation between a doctor and a rare disease in the rare disease list is extracted based on electronic medical record data in a preset time period, and then a weight coefficient and a punishment coefficient of the doctor for the rare disease are determined based on the corresponding relation; and finally, determining recommended information of the doctor for the rare disease based on the weight coefficient and the punishment coefficient.
Fig. 3 schematically illustrates a flowchart of a method for recommending doctor information according to another embodiment of the present disclosure, where the method provided by the embodiment of the present disclosure may be processed by any electronic device having computing processing capabilities, such as the server 105 and/or the terminal devices 102, 103 in the embodiment of fig. 1, and in the following embodiment, the server 105 is taken as an execution body for illustration, but the present disclosure is not limited thereto.
As shown in fig. 3, the method for recommending doctor information provided by the embodiment of the present disclosure may include the following steps.
In step S310, a doctor recommendation request including a first disease is received.
In the embodiment of the present invention, the doctor recommendation request may further include a target area, but the present invention is not limited thereto, and for example, the doctor recommendation request may further include a target preset time period.
In step S320, recommendation information for all doctors for the first disease is determined.
In the embodiment of the invention, based on the determined recommended information of each doctor for the first disease, the recommended information of the doctor for the first disease can be determined. If the doctor recommendation request carries the target area, the doctor recommendation information corresponding to the first disease in the target area can be determined. If the doctor recommendation request does not carry the target area, the doctor recommendation information corresponding to the first disease in the area corresponding to the position information can be determined based on the position information of the initiator of the request, or the doctor recommendation information corresponding to the first disease in the target area set by user definition.
If the recommendation information of each doctor for each first disease is not determined, extracting and determining a corresponding relationship between each doctor and at least one disease of the diseases based on electronic medical record data in a preset time period (if the recommendation request of the doctor is not carried, the calculation is performed according to a preset value) of the determined target area, and then determining a weight coefficient of each doctor for the target first disease based on the corresponding relationship; and determining penalty coefficients of the doctors based on the corresponding relation; and finally, determining doctor recommendation information of each doctor for the target first disease based on the weight coefficient and the punishment coefficient, and further determining doctor recommendation information for the first disease according to the recommendation information of each doctor for the first disease.
In step S330, the recommended information of all doctors for the first disease is displayed to the patient after being arranged according to a preset sequence.
According to the embodiment of the invention, the recommendation information can be recommendation coefficients, after the recommendation information of all doctors is obtained, the recommendation coefficients of all doctors are sequenced from high to low, the cut-out part is displayed to the patient, and when the recommendation information is displayed, the information of the doctors can be displayed according to a preset or customized format.
In the embodiment of the invention, the corresponding relation between doctors and at least one disease is determined based on medical record data in a preset time period; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient. The method and the device realize that the doctor recommendation information is determined based on the weight coefficient and the punishment coefficient, wherein the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the punishment coefficient is determined based on the types of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the types of diagnosing the disease by the doctor are comprehensively considered during the doctor recommendation, the automatic recommendation of the doctor can be realized, the recommendation efficiency of the doctor is improved, and the recommendation accuracy of the doctor is improved.
The following describes embodiments of apparatus of the present disclosure that may be used to perform the above-described doctor information recommendation apparatus of the present disclosure. For details not disclosed in the embodiments of the device of the present disclosure, please refer to the embodiments of the method for standardizing drug information described in the present disclosure.
Fig. 4 schematically illustrates a block diagram of an apparatus for physician information recommendation according to an embodiment of the present disclosure.
Referring to fig. 4, an apparatus 400 for recommending doctor information according to an embodiment of the present disclosure may include: a data input module 410, a coefficient determination module 420, and an information determination module 430.
The data input module 410 may be configured to determine a correspondence of a doctor to at least one disease based on medical record data over a preset period of time.
A coefficient determination module 420, which may be configured to determine a weight coefficient of the doctor for a first disease and a penalty coefficient of the doctor based on the correspondence; wherein the at least one disease comprises the first disease.
The information determination module 430 may be configured to determine recommended information of the physician for the first disease based on the weight coefficient and the penalty coefficient.
In the embodiment of the invention, the corresponding relation between doctors and at least one disease is determined based on medical record data in a preset time period; determining a weight coefficient of the doctor for a first disease based on the correspondence and a penalty coefficient of the doctor; wherein the at least one disease comprises the first disease; and determining recommended information of the doctor for the first disease based on the weight coefficient and the punishment coefficient. The method and the device realize that the doctor recommendation information is determined based on the weight coefficient and the punishment coefficient, wherein the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the punishment coefficient is determined based on the types of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the types of diagnosing the disease by the doctor are comprehensively considered during the doctor recommendation, the automatic recommendation of the doctor can be realized, the recommendation efficiency of the doctor is improved, and the recommendation accuracy of the doctor is improved.
Fig. 5 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure. It should be noted that the computer system 500 of the electronic device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present disclosure.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described below with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. When executed by a Central Processing Unit (CPU) 501, performs the various functions defined in the system of the present application.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units involved in the embodiments of the present disclosure may be implemented in software, or may be implemented in hardware, and the described modules and/or units may also be disposed in a processor. Wherein the names of the modules and/or units do not in some cases constitute limitations on the modules and/or units themselves.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by one of the electronic devices, cause the electronic device to implement the methods described in the embodiments below. For example, the electronic device may implement the various steps shown in fig. 2 or 3.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (6)
1. A method for recommending doctor information, comprising:
determining a correspondence of a doctor to at least one disease based on medical record data within a preset time period;
determining a weight coefficient and a penalty coefficient of the doctor for the first disease based on the correspondence; wherein the at least one disease comprises the first disease;
determining recommended information of the doctor for the first disease based on the weight coefficient and the penalty coefficient;
Wherein the determining the weight coefficient of the doctor for the first disease based on the correspondence includes: determining a plurality of sub-time periods included in the preset time period according to the preset time period; determining the times that the doctor diagnoses the first disease in each sub-time period in the preset time period according to the corresponding relation; determining a weight coefficient of the doctor for the first disease according to the times of the doctor diagnosing the first disease in each sub-time period;
wherein the determining the weight coefficient of the doctor for the first disease according to the number of times that the doctor diagnoses the first disease in each sub-time period comprises: determining a sub-weight coefficient of the doctor for the disease in each sub-time period according to the times that the doctor diagnoses the first disease in each sub-time period; determining a weight coefficient of the doctor for the first disease according to a sub-weight coefficient of the doctor for the first disease in each sub-time period;
wherein the determining a penalty factor for the doctor for the first disease based on the correspondence comprises: determining a plurality of sub-time periods included in the preset time period according to the preset time period; based on the corresponding relation, determining the number of diseases corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period; determining a punishment coefficient of the doctor based on the number of diseases corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period;
The determining, based on the correspondence, the number of diseases corresponding to the doctor in the preset time period and the sub-penalty coefficient of the doctor in each sub-time period in the preset time period includes: determining a sub-punishment coefficient of the doctor in each sub-time period based on the disease duplication removal number corresponding to the doctor in each sub-time period in the preset time period;
wherein the weight coefficient is calculated by the following formula:
the penalty factor is calculated by the following formula:
wherein DF (p, d) represents the weight coefficient of doctor p for the first disease d, N i,p,d The number of times of the first disease D corresponding to the ith doctor p from the current time is represented, IDF (p) represents the punishment coefficient of doctor p, D represents the number of diseases corresponding to doctor p in a preset time period, D i,p Represents the number of patients with the disease corresponding to the ith doctor p from the current time after the duplication removal, D i,p Less than or equal to D, gamma represents an attenuation coefficient, is constant, and is less than 1 i N i,p,d Sub-weight coefficient, gamma, representing the current time from the ith physician p for the first disease d i D i,p A sub-penalty coefficient representing the doctor p of the ith year from the current time, n representing the preset timeThe interval is an integer greater than or equal to 1.
2. The method of claim 1, wherein the method further comprises:
determining recommended information of each doctor for the first disease based on medical record data in the preset time period;
and arranging the recommended information of each doctor aiming at the first disease according to a preset sequence, and displaying the recommended information to the patient so as to provide the doctor aiming at the first disease for the patient.
3. The method of claim 1, wherein determining a doctor's correspondence to at least one disease based on medical record data over a predetermined period of time comprises:
acquiring hospitalization medical records from case data in a preset time period;
and extracting the doctor and at least one disease diagnosed by the doctor from the hospitalized medical records.
4. An apparatus for recommending doctor information, comprising:
the data input module is configured to determine the corresponding relation between doctors and at least one disease based on medical record data in a preset time period;
a coefficient determination module configured to determine a weight coefficient and a penalty coefficient of the doctor for a first disease based on the correspondence; wherein the at least one disease comprises the first disease;
an information determination module configured to determine recommended information of the doctor for the first disease based on the weight coefficient and the penalty coefficient;
The coefficient determining module is further configured to determine a plurality of sub-time periods included in the preset time period according to the preset time period; determining the times that the doctor diagnoses the first disease in each sub-time period in the preset time period according to the corresponding relation; determining a weight coefficient of the doctor for the first disease according to the times of the doctor diagnosing the first disease in each sub-time period; wherein the determining the weight coefficient of the doctor for the first disease according to the number of times that the doctor diagnoses the first disease in each sub-time period comprises: determining a sub-weight coefficient of the doctor for the disease in each sub-time period according to the times that the doctor diagnoses the first disease in each sub-time period; determining a weight coefficient of the doctor for the first disease according to a sub-weight coefficient of the doctor for the first disease in each sub-time period;
the coefficient determining module is further configured to determine a plurality of sub-time periods included in the preset time period according to the preset time period; based on the corresponding relation, determining the number of diseases corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period; determining a punishment coefficient of the doctor based on the number of diseases corresponding to the doctor in the preset time period and the sub-punishment coefficient of the doctor in each sub-time period in the preset time period; the determining, based on the correspondence, the number of diseases corresponding to the doctor in the preset time period and the sub-penalty coefficient of the doctor in each sub-time period in the preset time period includes: determining a sub-punishment coefficient of the doctor in each sub-time period based on the disease duplication removal number corresponding to the doctor in each sub-time period in the preset time period;
The coefficient determination module is further configured to calculate the weight coefficient by the following formula:
the penalty factor is calculated by the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,DF (p, d) represents the weight coefficient of doctor p for the first disease d, N i,p,d The number of times of the first disease D corresponding to the ith doctor p from the current time is represented, IDF (p) represents the punishment coefficient of doctor p, D represents the number of diseases corresponding to doctor p in a preset time period, D i,p Represents the number of patients with the disease corresponding to the ith doctor p from the current time after the duplication removal, D i,p Less than or equal to D, gamma represents an attenuation coefficient, is constant, and is less than 1 i N i,p,d Sub-weight coefficient, gamma, representing the current time from the ith physician p for the first disease d i D i,p And (3) a sub-penalty coefficient of the doctor p in the ith year from the current time is represented, and n represents a preset time period and is an integer greater than or equal to 1.
5. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 3.
6. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010338556.7A CN111554387B (en) | 2020-04-26 | 2020-04-26 | Doctor information recommendation method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010338556.7A CN111554387B (en) | 2020-04-26 | 2020-04-26 | Doctor information recommendation method and device, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111554387A CN111554387A (en) | 2020-08-18 |
| CN111554387B true CN111554387B (en) | 2023-05-23 |
Family
ID=72000315
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010338556.7A Active CN111554387B (en) | 2020-04-26 | 2020-04-26 | Doctor information recommendation method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111554387B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN109582797A (en) * | 2018-12-13 | 2019-04-05 | 泰康保险集团股份有限公司 | Obtain method, apparatus, medium and electronic equipment that classification of diseases is recommended |
| CN109637651A (en) * | 2018-10-31 | 2019-04-16 | 北京春雨天下软件有限公司 | More doctor's recommended methods and device, online consultation system |
| WO2020046173A1 (en) * | 2018-08-27 | 2020-03-05 | Михаил Борисович БОГДАНОВ | Automated system to support medical decisions in combined pathology |
| CN111009316A (en) * | 2019-12-25 | 2020-04-14 | 福州大学 | Doctor-patient matching method based on Bayesian network |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2559241C (en) * | 2004-03-12 | 2015-10-13 | Olivier Saidi | Systems and methods for treating, diagnosing and predicting the occurrence of a medical condition |
| CA2702720A1 (en) * | 2010-05-04 | 2011-11-04 | Mor Research Applications Ltd | A multi-phase anchor-based diagnostic decision-support method and system |
| EP3225697A3 (en) * | 2010-12-30 | 2017-11-22 | Foundation Medicine, Inc. | Optimization of multigene analysis of tumor samples |
| CN103164804B (en) * | 2011-12-16 | 2016-11-23 | 阿里巴巴集团控股有限公司 | The information-pushing method of a kind of personalization and device |
| US9946839B1 (en) * | 2013-09-10 | 2018-04-17 | MD Insider, Inc. | Search engine systems for matching medical providers and patients |
| CN103559637B (en) * | 2013-11-13 | 2017-01-11 | 王竞 | Method and system for recommending doctor for patient |
| CN107705842B (en) * | 2017-10-13 | 2019-09-03 | 合肥工业大学 | Intelligent diagnosis system and its working method |
| CN107863134A (en) * | 2017-11-24 | 2018-03-30 | 郑州云海信息技术有限公司 | A kind of Intelligent medical management system based on cloud computing |
| CN108305675B (en) * | 2018-01-26 | 2020-10-23 | 合肥工业大学 | Intelligent diagnosis guiding method and system with enhanced diversity |
| CN108877946A (en) * | 2018-05-04 | 2018-11-23 | 浙江工业大学 | A kind of doctor's expert recommendation method based on network characterization |
| CN109473153A (en) * | 2018-10-30 | 2019-03-15 | 医渡云（北京）技术有限公司 | Processing method, device, medium and the electronic equipment of medical data |
| CN109508336A (en) * | 2019-01-24 | 2019-03-22 | 易保互联医疗信息科技（北京）有限公司 | Search method, storage medium and computer equipment based on medical resource factbase |
| CN110232971B (en) * | 2019-05-24 | 2022-04-12 | 深圳市翩翩科技有限公司 | Doctor recommendation method and device |
| CN110993081B (en) * | 2019-12-03 | 2023-08-11 | 济南大学 | Doctor online recommendation method and system |
| CN111062193B (en) * | 2019-12-16 | 2023-04-25 | 医渡云（北京）技术有限公司 | Medical data labeling method and device, storage medium and electronic equipment |
-
2020
- 2020-04-26 CN CN202010338556.7A patent/CN111554387B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| WO2020046173A1 (en) * | 2018-08-27 | 2020-03-05 | Михаил Борисович БОГДАНОВ | Automated system to support medical decisions in combined pathology |
| CN109637651A (en) * | 2018-10-31 | 2019-04-16 | 北京春雨天下软件有限公司 | More doctor's recommended methods and device, online consultation system |
| CN109582797A (en) * | 2018-12-13 | 2019-04-05 | 泰康保险集团股份有限公司 | Obtain method, apparatus, medium and electronic equipment that classification of diseases is recommended |
| CN111009316A (en) * | 2019-12-25 | 2020-04-14 | 福州大学 | Doctor-patient matching method based on Bayesian network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111554387A (en) | 2020-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Avati et al. | Improving palliative care with deep learning | |
| US11670415B2 (en) | Data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations | |
| CN107767929B (en) | Case report form filling method and device, electronic equipment and storage medium | |
| US20140006044A1 (en) | System and method for preparing healthcare service bundles | |
| CN111785371A (en) | Medical service method, medical service device, computer equipment and storage medium | |
| CN110265099B (en) | Method and device for outputting medical records | |
| CN111161813A (en) | Method, device and equipment for processing chronic disease information and storage medium | |
| US20170132367A1 (en) | Multifarious medical consultation options optimizer | |
| US20230154582A1 (en) | Dynamic database updates using probabilistic determinations | |
| CN110675952A (en) | Checking decision method and device, terminal equipment and computer readable storage medium | |
| US20110153344A1 (en) | Methods and apparatus for integrated medical case research and collaboration | |
| CN112465172A (en) | Hospital intelligent treatment method and device | |
| CN111554387B (en) | Doctor information recommendation method and device, storage medium and electronic equipment | |
| Hoyos et al. | Post-trauma follow-up phone call shows lower readmission rates for patients discharged from emergency department compared to inpatient stay | |
| US11232411B2 (en) | Integrated healthcare system | |
| CN113053479A (en) | Medical data processing method, device, medium and electronic equipment | |
| CN113053531B (en) | Medical data processing method, medical data processing device, computer readable storage medium and equipment | |
| CN111899844B (en) | Sample generation method and device, server and storage medium | |
| CN114496139A (en) | Quality control method, device, equipment and system of electronic medical record and readable medium | |
| CN112699872A (en) | Form auditing processing method and device, electronic equipment and storage medium | |
| CN111128330A (en) | Automatic entry method and device for electronic case report table and related equipment | |
| CN112488857A (en) | Event recognition method and device, electronic equipment and storage medium | |
| CN111651965A (en) | Document generation method and device, electronic equipment and computer readable medium | |
| US20130035959A1 (en) | Methods and systems for tracking medical care | |
| CN111653328B (en) | Medical record information pushing method and device, storage medium and electronic equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |