CN111554387A - 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
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The embodiment of the disclosure provides a doctor information recommendation method, a doctor information recommendation device, a storage medium and electronic equipment, and relates to the technical field of computer technology and information processing. The method comprises the following steps: determining a corresponding relation between a doctor and at least one disease based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient. The doctor recommendation information is determined based on the weight coefficient and the penalty coefficient, the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the penalty coefficient is determined based on the type of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the type of diagnosing the disease by the doctor are comprehensively considered when the doctor recommends, automatic recommendation of the doctor can be achieved, the doctor recommendation efficiency is improved, and the doctor recommendation accuracy is improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies and information processing technologies, and in particular, to a method and an apparatus for recommending doctor information, a storage medium, and an electronic device.
Background
Today, electronic informatization is an important function for doctor recommendation in an internet hospital in one area.
In the related art, the following method is generally adopted for doctor recommendation: one is manual recommendation, such as by a doctor public praise. The other is according to the recommendation of the doctor receptionist.
In the above techniques, all diseases and doctors cannot be covered by manual recommendation, and the criteria and results recommended by different people are not consistent. According to the doctor receptionist recommendation, a doctor who receives the highest degree of a specific disease is not necessarily the most specialized for the disease, and a plurality of new experts and new technologies exist every year, and the influence of time on the recommendation is not considered.
It is to be noted that the information disclosed in the above background section is only for enhancement of 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 and a device for recommending doctor information, a computer readable medium and an electronic device, so that the flexibility and the accuracy of recommending the doctor information are improved at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of an embodiment of the present disclosure, a method for recommending doctor information is provided, where the method includes: determining a corresponding relation between a doctor and at least one disease based on medical record data in a preset time period; determining a weight coefficient and a penalty coefficient of the doctor for the first disease based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient.
In some exemplary embodiments of the present disclosure, determining the weighting factor of the doctor for the first disease based on the correspondence based on the foregoing scheme includes: determining a plurality of sub-time periods included in the preset time period according to the preset time period; determining the times of diagnosing the first disease by the doctor in each sub-time period within the preset time period according to the corresponding relation; determining a weighting factor 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 disclosure, based on the foregoing scheme, determining the weighting factor of the doctor for the first disease according to the number of times that the doctor diagnosed the first disease in each sub-time period includes: determining a sub-weight coefficient of the doctor for the disease in each sub-time period according to the number of times that the doctor diagnoses the first disease in each sub-time period; determining the weighting coefficient of the doctor for the first disease according to the sub-weighting coefficient of the doctor for the first disease in each sub-time period.
In some exemplary embodiments of the present disclosure, determining a penalty factor for the first disease based on the correspondence based on the aforementioned scheme includes: determining a plurality of sub-time periods included in the preset time period according to the preset time period; determining 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 based on the corresponding relation; and determining the penalty coefficient of the doctor 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 some exemplary embodiments of the present disclosure, based on the foregoing scheme, determining, based on the correspondence, a 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 the sub-penalty coefficients of the doctors in each sub-time period based on the disease duplication eliminating numbers corresponding to the doctors in each sub-time period in the specified preset time period.
In some exemplary embodiments of the present disclosure, based on the foregoing, the method further includes: determining recommendation information of each doctor for the first disease based on medical record data in the preset time period; and arranging the recommendation information of the doctors for the first disease according to a preset sequence and displaying the recommendation information to the patient so as to provide the doctor for the first disease for the patient.
In some exemplary embodiments of the present disclosure, based on the foregoing scheme, determining correspondence between a doctor and at least one disease based on medical record data in a preset time period includes: acquiring a medical record of hospitalization from case data in a preset time period; extracting the doctor and at least one disease diagnosed by the doctor from the medical record of the hospitalization.
According to an aspect of an embodiment 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 a doctor 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 recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient.
In some exemplary embodiments of the present disclosure, based on the foregoing scheme, the coefficient determining module includes: a first determining unit configured to determine a plurality of sub-periods included in the preset period according to the preset period; the second determining unit is configured to determine the number of times that the doctor diagnoses the first disease in each sub-time period in the preset time period according to the corresponding relation; a third determining unit configured to determine a weighting factor 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 disclosure, based on the foregoing scheme, 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 that the doctor diagnoses the first disease in each sub-period; determining the weighting coefficient of the doctor for the first disease according to the sub-weighting coefficient of the doctor for the first disease in each sub-time period.
In some exemplary embodiments of the present disclosure, based on the foregoing scheme, the coefficient determining module further includes: a fourth determining unit configured to determine a plurality of sub-periods included in the preset period according to the preset period; a fifth determining unit, configured to determine, based on the correspondence, a 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; a sixth determining unit, configured to determine a penalty coefficient of the doctor 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 some exemplary embodiments of the disclosure, based on the foregoing scheme, the fifth determining unit is configured to determine the sub-penalty coefficient of the doctor in each sub-period based on the corresponding disease deduplication number of the doctor in each sub-period in the specified preset period.
In some exemplary embodiments of the present disclosure, based on the foregoing, the apparatus further includes: the doctor recommending module is configured to determine recommending information of each doctor for the first disease based on medical record data in the preset time period; and arranging the recommendation information of the doctors for the first disease according to a preset sequence and displaying the recommendation information to the patient so as to provide the doctor for the first disease for the patient.
In some exemplary embodiments of the present disclosure, based on the foregoing scheme, the data input module is configured to acquire an in-patient medical record from case data within a preset time period; extracting the doctor and at least one disease diagnosed by the doctor from the hospitalized medical record.
According to an aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program, wherein the computer program is configured to implement the method as described in the above embodiments when executed by a processor.
According to an aspect of an embodiment of the present disclosure, there is provided an electronic device including: one or more processors; 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 embodiments above.
In the embodiment of the invention, the corresponding relation between a doctor and at least one disease is determined based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient. The doctor recommendation information is determined based on the weight coefficient and the penalty coefficient, the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the penalty coefficient is determined based on the type of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the type of diagnosing the disease by the doctor are comprehensively considered when the doctor recommends, automatic recommendation of the doctor can be achieved, the doctor recommendation efficiency is improved, and the doctor recommendation accuracy 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 present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
In the drawings:
FIG. 1 illustrates a schematic diagram of an exemplary system architecture 100 to which the method or apparatus of doctor information recommendation of embodiments of the present disclosure may be applied;
FIG. 2 schematically shows a flow diagram of a method of physician information recommendation, according to one embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of a method of physician information recommendation in accordance with another embodiment of the present disclosure;
FIG. 4 schematically shows a block diagram of an apparatus for physician information recommendation in accordance with an embodiment of the present disclosure;
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different 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 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 subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to 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 actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture 100 to which the method or apparatus for doctor information recommendation of the 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 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
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, server 105 may be a server cluster comprised of multiple servers, or the like.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may be various electronic devices having a display screen, including but not limited to smart phones, tablet computers, portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services. For example, the terminal device 103 (which 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 relationship between a doctor and at least one disease based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; the recommendation information of the doctors for the first disease is determined based on the weighting coefficient and the penalty coefficient, then the recommendation information of each doctor for the first disease is integrated, the recommendation information of each doctor for the first disease is determined from the recommendation information of each doctor for the first disease, and the recommendation information of each doctor is sent to the terminal 103, and the terminal 103 can display the recommendation information of each doctor.
Fig. 2 schematically shows a flowchart of a method of physician information recommendation according to one embodiment of the present disclosure. The method provided by the embodiment of the present disclosure may be processed by any electronic device with computing processing capability, for example, the server 105 and/or the terminal devices 102 and 103 in the embodiment of fig. 1 described above, and in the following embodiment, the server 105 is taken as an execution subject for example, but the present disclosure is not limited thereto.
As shown in fig. 2, a method for recommending doctor information provided by an embodiment of the present disclosure may include the following steps:
in step S210, the correspondence relationship between the doctor and at least one disease is determined based on medical record data in a preset time period.
In the embodiment of the present disclosure, the doctor information recommendation may be a doctor information recommendation for a target area, where the target area may be a hospital, a city, a country, and the like. For example, if the correspondence between doctors and diseases is determined based on medical record data of Beijing in a preset time period, the recommended information of doctors in Beijing can be determined.
It should be noted that, in the correspondence relationship between the determined doctor and the at least one disease according to the embodiment of the present invention, the doctor may be any one of all doctors included in medical record data in a preset time period, and recommendation information of each doctor for the first disease may be determined in a polling manner.
It should be noted that, when recording the correspondence between a doctor and at least one disease, the information of the doctor, such as the doctor's name, the hospital, the work history, the achievement obtained, the contact information, the stage of the disease with good excellence, and the like, 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 in-patient medical record can be acquired from the case data, and the doctor and at least one disease diagnosed by the doctor can be extracted from the in-patient medical record.
In the embodiment of the present invention, the in-patient medical record may be divided into a discharge diagnosis and an admission diagnosis, when the doctor and at least one disease diagnosed by the doctor are extracted from the in-patient medical record, all records of the doctor for which the treating doctor is the main doctor may be extracted from the discharge diagnosis of the doctor, the disease suffered by the patient may be extracted from the admission diagnosis corresponding to each discharge diagnosis of the doctor by the main doctor, and the disease may be normalized according to the 10 th revision (abbreviated as ICD-10) of International Classification of Diseases (ICD) to extract the disease (at least one disease) corresponding to each admission diagnosis. Thereby extracting the doctor and at least one disease diagnosed by the doctor.
It should be noted that other criteria may be used in the stratification of the disease from which the patient is hospitalized. If the disease and the doctor are already included in the admission diagnosis or the discharge diagnosis, the doctor and the disease diagnosed by the doctor can be directly extracted only by the admission diagnosis or the discharge diagnosis.
It should be noted that each inpatient medical record can extract at least one doctor and the disease that the doctor diagnoses.
In this embodiment of the present invention, the preset time period may refer to years, for example, 5 years, before the current time (or a preset time), and assuming that the current time is 2020, the preset time period refers to 2015-2020. The correspondence between a doctor and at least one disease is extracted based on medical record data in a preset time period, and the correspondence between the doctor and at least one disease mainly treated by the doctor can be extracted from medical record data in hospitals from a certain region in 2015 to 2020.
In step S220, a weighting coefficient and a penalty coefficient for the doctor for a first disease are determined based on the correspondence, wherein the at least one disease includes the first disease.
It is noted that the first disease may be any one of at least one disease corresponding to the doctor.
In the embodiment of the present invention, a plurality of sub-time periods included in a preset time period may 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 3 months and 2 days in 2020, the 5 years may be divided into 5 sub-time periods, which are:
the corresponding specific time is 3/2019 to 3/2/2020/1 from the current time; in the 2 nd year from the current time, the corresponding specific time is 3/2018 to 3/2/2019; in the 3 rd year from the current time, the corresponding specific time is 3 months and 3 days in 2017 to 3 months and 2 days in 2018; in the 4 th year from the current time, the corresponding specific time is 3/2016 to 3/2/2017; and 5 th year from the current time, the corresponding specific time is 3/2015 to 3/2016 and 2/3/2016.
In an embodiment of the present invention, after determining a plurality of sub-time periods included in a preset time period, based on the correspondence, the number of times that the doctor has diagnosed the first disease in each sub-time period in the preset time period may be determined, and a weight coefficient of the doctor for the first disease may be determined according to the number of times that the doctor has diagnosed the first disease in each sub-time period.
In the embodiment of the present invention, the weight coefficient refers to the importance degree of a certain factor or index with respect to a certain event, and in the embodiment of the present invention, the weight coefficient refers to the importance degree of a first disease with respect to all diseases that the doctor treats, and if the weight coefficient of a certain doctor with respect to a first disease is higher, it indicates that the doctor treats the first disease more frequently and is recommended more easily. The sub-weight factor refers to the importance of the first disease relative to all the diseases treated by the physician within the sub-period.
According to the embodiment of the invention, the sub-weight coefficient of the doctor for the disease in each sub-period can be determined according to the number of times that the doctor diagnoses the first disease in each sub-period, and then the weight coefficient of the doctor for the first disease in each sub-period can be determined according to the sub-weight coefficient of the doctor for the first disease.
The embodiment of the invention provides a method for determining a weight coefficient of each doctor for each disease, and taking a preset time period as the previous 5 years from the current time as an example, the weight coefficient of a doctor p for a first disease d can be determined by the following formula:
where DF (p, d) represents the weighting factor of doctor p for the first disease d, Ni,p,dRepresenting the number of times of the first disease d corresponding to the ith year doctor p from the current time, gamma representing an attenuation coefficient, which is a constant, e.g., 0.7, may be obtained based on experimental data, wherein gamma isiNi,p,dRepresents the sub-weight coefficients for the first disease d from the ith year doctor p at the current time.
It should be noted that, as shown in formula (1), the more times doctor p treats the first disease d, the higher the weighting factor is, and the easier the follow-up is to be recommended. In the embodiment of the invention, the more times of treating a certain disease, the more easily recommended rule of a doctor is reflected in the calculation process of the weight coefficient, so that doctor recommendation on the basis of the diagnosis times of the disease by the doctor is realized.
It should be noted that, as shown in formula (1), as i increases, the longer the time is from the current time, and since γ is less than 1, γ corresponds to γiSmaller and smaller, e.g. γ is 0.7, when i is 1, γ isiWhen i is 2, then γ is 0.7i0.49. According to the embodiment of the invention, the calculation of doctor weight based on time is realized, the closer the doctor weight is to the current time, the higher the doctor weight is, the weight coefficient of the doctor for diagnosing diseases in the near term is improved, the relation of the change of the doctor ability along with the time is reflected to the calculation process of the weight coefficient, the doctor recommendation based on the change of the time is realized, and the reliability and the accuracy of the weight coefficient are improved.
According to an embodiment of the present invention, the weighting factor of the doctor for the first disease may be determined based on the above formula (1).
According to the embodiment of the present invention, a plurality of sub-time periods included in the preset time period may be determined according to a 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 may be determined based on the correspondence, 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 penalty coefficient and the sub-penalty coefficient are both used for representing the repetition degree of the disease corresponding to the doctor, if the disease repetition degree corresponding to the doctor is higher, the sub-penalty coefficient is lower, and the penalty coefficient is higher, the doctor is a specialist doctor, and the doctor is easier to recommend.
It should be noted that each sub-period of the preset time period may be the same as or different from each sub-period in the determination of the weight coefficient.
In the embodiment of the present invention, after each sub-period of the preset period is determined, the sub-penalty coefficient of each doctor in each sub-period may be determined based on the number of the disease deduplication corresponding to the doctor in each sub-period of the preset period, and then the penalty coefficient of the doctor is determined based on the number of the disease corresponding to the doctor in the preset period and the sub-penalty coefficient of the doctor in each sub-period of the preset period.
The embodiment of the invention provides a method for determining the penalty coefficients of doctors, which takes a preset time period as an example of the previous 5 years from the current time, and can determine the penalty coefficients of doctors p through the following formula:
wherein IDF (p) represents the penalty coefficient of doctor p, D represents the number of diseases corresponding to doctor p in a preset time period, Di,pRepresents the number of deduplicated diseases corresponding to the ith-year doctor p from the current time，Di,pD or less, and gamma represents an attenuation coefficient, and is a constant, such as 0.7, and can be obtained based on experimental data, wherein gamma isiDi,pA sub-penalty factor representing the ith year doctor p from the current time.
As can be seen from the formula (2), the number of disease categories corresponding to the doctor p in the preset time period is a constant value, and is the sum of the number of all disease categories corresponding to the doctor. When the repeatability of the corresponding disease type in a certain sub-time period of a doctor is higher, and the number after the duplication removal (the number of the remaining diseases after the duplication removal) is lower, the sub-penalty coefficient in the sub-time period is lower, and the sum of the sub-penalty coefficients is lower, when the penalty coefficients are calculated subsequently, the value obtained by 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, and if the repeatability of the disease type is higher, D is larger than the sum of the sub-penalty coefficients), the penalty coefficient obtained by taking the logarithm is higher, and when the doctor recommendation information is determined, the value obtained by multiplying the penalty coefficient by the weight is higher, and the doctor is easier to recommend. In the embodiment of the invention, the doctor is recommended based on the special department and the general department by considering that all diseases of the primary outpatient or general doctor can be seen and the diseases corresponding to the special doctor are relatively repeated, and reflecting the rule that the doctor is more likely to recommend the more repeated diseases corresponding to the doctor to the calculation process of the penalty coefficient.
According to the embodiment of the invention, the penalty coefficient of the doctor can be determined based on the formula (2). It should be noted that the physician's penalty factor is the same for each disease during the predetermined time period.
In S230, recommendation information of 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 recommendation information of each doctor for the first disease extracted from the case data can be determined based on the medical record data in the preset time period, and the recommendation information of each doctor for the first disease is displayed to the patient after being arranged according to the preset sequence, so that the doctor for the first disease is provided for the patient.
The recommendation information of the doctor for the first disease may be a recommendation coefficient (recommendation degree value), and after the recommendation coefficients of the doctors for the first disease are obtained, the doctors may be arranged in the order of the recommendation coefficients from high to low and displayed to the patient.
According to the embodiment of the invention, the recommendation coefficient of a doctor can be determined by multiplying the weight coefficient by the penalty coefficient. For example, using DF (p, d) × idf (p) in the above formula, the recommended coefficient for the first disease d by the doctor p can be obtained. After the recommendation coefficients of doctors for the first disease are determined, doctor recommendation information for the first disease is determined according to the recommendation coefficients of the diseases in the order from high to low.
It should be noted that the doctor who presents may be a doctor who cuts a part from high to low according to the recommended factor of the first disease. The physician information may be displayed in a preset or custom format. The doctor's information may include, but is not limited to, doctor name, hospital, work history, achievements obtained, contact details, and stage of illness that is skilled.
In the embodiment of the invention, the corresponding relation between a doctor and at least one disease is determined based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient. The doctor recommendation information is determined based on the weight coefficient and the penalty coefficient, the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the penalty coefficient is determined based on the type of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the type of diagnosing the disease by the doctor are comprehensively considered when the doctor recommends, automatic recommendation of the doctor can be achieved, the doctor recommendation efficiency is improved, and the doctor recommendation accuracy is improved.
In an embodiment of the invention, a rare disease list can be preset, a corresponding relation between a doctor and a certain 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 penalty coefficient of the doctor for the rare disease are determined based on the corresponding relation; and finally, determining recommendation information of the doctor for the rare disease based on the weight coefficient and the penalty coefficient.
Fig. 3 schematically shows a flowchart of a method for recommending doctor information according to another embodiment of the present disclosure, and the method provided by the embodiment of the present disclosure may be processed by any electronic device with computing processing capability, such as the server 105 and/or the terminal devices 102 and 103 in the above embodiment of fig. 1, in the following embodiment, the server 105 is taken as an execution subject for illustration, but the present disclosure is not limited thereto.
As shown in fig. 3, a 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 to this, and for example, the doctor recommendation request may further include a target preset time period.
In step S320, recommendation information for all doctors of the first disease is determined.
In the embodiment of the invention, after the recommendation information of each doctor aiming at the first disease is determined, the recommendation information of the doctor aiming at the first disease can be determined. If the doctor recommendation request carries the target area, doctor recommendation information corresponding to the first disease in the target area can be determined from the target area. 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 may 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 a user in a self-defined manner.
If the recommendation information of each doctor for each first disease is not determined, the correspondence between each doctor and at least one disease of the disease may be extracted and determined based on the electronic medical record data in the preset time period of the determined target area (if the recommendation request of the doctor is not carried, the calculation is performed by using a preset value), and then the weight coefficient of each doctor for the target first disease is determined based on the correspondence; and the punishment coefficient of each doctor is determined based on the corresponding relation; and finally, determining doctor recommendation information of each doctor for the target first disease based on the weighting coefficient and the penalty coefficient, and further determining the doctor recommendation information for the first disease according to the recommendation information of each doctor for the first disease.
In step S330, the recommendation 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 ranked from high to low, and then a part of the recommendation coefficients are displayed to the patient, and during displaying, the information of the doctors can be displayed according to a preset or self-defined format.
In the embodiment of the invention, the corresponding relation between a doctor and at least one disease is determined based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient. The doctor recommendation information is determined based on the weight coefficient and the penalty coefficient, the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the penalty coefficient is determined based on the type of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the type of diagnosing the disease by the doctor are comprehensively considered when the doctor recommends, automatic recommendation of the doctor can be achieved, the doctor recommendation efficiency is improved, and the doctor recommendation accuracy is improved.
Embodiments of the disclosed apparatus are described below, which may be used to implement the above-described doctor information recommendation apparatus of the present disclosure. For the details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method for standardizing the medicine information described above in the present disclosure.
Fig. 4 schematically shows 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 physician information recommendation 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 can be configured to determine a correspondence between a doctor and at least one disease based on medical record data within a preset time period.
A coefficient determination module 420, which may be configured to determine a weighting 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.
An information determination module 430 may be configured to determine recommendation information of the physician for the first disease based on the weighting coefficient and the penalty coefficient.
In the embodiment of the invention, the corresponding relation between a doctor and at least one disease is determined based on medical record data in a preset time period; determining a weighting coefficient of the doctor for the first disease and a penalty coefficient of the doctor based on the corresponding relation; wherein the at least one disease comprises the first disease; determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient. The doctor recommendation information is determined based on the weight coefficient and the penalty coefficient, the weight coefficient is determined based on the times of diagnosing the first disease by the doctor, and the penalty coefficient is determined based on the type of diagnosing the disease by the doctor, so that the times of diagnosing the first disease by the doctor and the type of diagnosing the disease by the doctor are comprehensively considered when the doctor recommends, automatic recommendation of the doctor can be achieved, the doctor recommendation efficiency is improved, and the doctor recommendation accuracy is improved.
FIG. 5 illustrates a schematic structural diagram of a computer system suitable for use in implementing an electronic device of an embodiment 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 bring any limitation to the functions and the scope of the application of the embodiments of the present disclosure.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that 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 necessary for system operation are also stored. The CPU501, ROM 502, and RAM 503 are connected to each other via 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 portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; 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 driver 510 is also connected to the I/O interface 505 as necessary. 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 necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, the processes described below with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 501.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present 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 contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 flowchart 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 described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described modules and/or units may also be disposed in a processor. Wherein the names of such modules and/or units do not in some way constitute a limitation on the modules and/or units themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 2 or fig. 3.
It should be noted that although in the above detailed description several modules or units of the 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, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, 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 (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute 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 variations, 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 will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for physician information recommendation, comprising:
determining a corresponding relation between a doctor and at least one disease based on medical record data in a preset time period;
determining a weight coefficient and a penalty coefficient of the doctor for the first disease based on the corresponding relation; wherein the at least one disease comprises the first disease;
determining recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient.
2. The method of claim 1, wherein determining a weighting factor for the physician for a 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;
determining the times of diagnosing the first disease by the doctor in each sub-time period within the preset time period according to the corresponding relation;
determining a weighting factor of the doctor for the first disease according to the number of times the doctor diagnoses the first disease in each sub-period.
3. The method of claim 2, wherein determining the weighting factor for the first disease by the physician based on the number of times the physician has diagnosed the first disease in each sub-period of time comprises:
determining a sub-weight coefficient of the doctor for the disease in each sub-time period according to the number of times that the doctor diagnoses the first disease in each sub-time period;
determining the weighting coefficient of the doctor for the first disease according to the sub-weighting coefficient of the doctor for the first disease in each sub-time period.
4. The method of claim 1, wherein determining a penalty factor for the physician 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;
determining 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 based on the corresponding relation;
and determining the penalty coefficient of the doctor 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.
5. The method of claim 4, wherein determining the number of diseases corresponding to the doctor in the preset time period and the sub-penalty factor of the doctor in each sub-time period in the preset time period based on the correspondence comprises:
and determining the sub-penalty coefficient of the doctor in each sub-time period based on the disease duplication elimination number corresponding to the doctor in each sub-time period in the preset time period.
6. The method of claim 1, wherein the method further comprises:
determining recommendation information of each doctor for the first disease based on medical record data in the preset time period;
and arranging the recommendation information of the doctors for the first disease according to a preset sequence and displaying the recommendation information to the patient so as to provide the doctor for the first disease for the patient.
7. The method of claim 1, wherein determining the correspondence between the doctor and the at least one disease based on medical record data over a predetermined period of time comprises:
acquiring a medical record of hospitalization from case data in a preset time period;
extracting the doctor and at least one disease diagnosed by the doctor from the medical record of the hospitalization.
8. An apparatus for doctor information recommendation, comprising:
the data input module is configured to determine the corresponding relation between a doctor 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 recommendation information of the doctor for the first disease based on the weight coefficient and the penalty coefficient.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114496191A (en) * | 2022-02-10 | 2022-05-13 | 北京京东拓先科技有限公司 | Medical information recommendation method, device, equipment and storage medium |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005091203A2 (en) * | 2004-03-12 | 2005-09-29 | Aureon Laboratories, Inc. | 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 |
| CN103164804A (en) * | 2011-12-16 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Personalized method and personalized device of information push |
| CN103559637A (en) * | 2013-11-13 | 2014-02-05 | 王竞 | Method and system for recommending doctor for patient |
| CN107705842A (en) * | 2017-10-13 | 2018-02-16 | 合肥工业大学 | Intelligent system for distribution of out-patient department and its method of work |
| 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 |
| US9946839B1 (en) * | 2013-09-10 | 2018-04-17 | MD Insider, Inc. | Search engine systems for matching medical providers and patients |
| 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 |
| CN108305675A (en) * | 2018-01-26 | 2018-07-20 | 合肥工业大学 | Intelligent hospital guide's method and system of diversity enhancing |
| 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 |
| 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 |
| CN110232971A (en) * | 2019-05-24 | 2019-09-13 | 深圳市翩翩科技有限公司 | A kind of doctor's recommended method and device |
| AU2019279918A1 (en) * | 2010-12-30 | 2020-01-16 | Foundation Medicine, Inc. | Optimization of multigene analysis of tumor samples |
| WO2020046173A1 (en) * | 2018-08-27 | 2020-03-05 | Михаил Борисович БОГДАНОВ | Automated system to support medical decisions in combined pathology |
| CN110993081A (en) * | 2019-12-03 | 2020-04-10 | 济南大学 | Doctor online recommendation method and system |
| CN111009316A (en) * | 2019-12-25 | 2020-04-14 | 福州大学 | Doctor-patient matching method based on Bayesian network |
| CN111062193A (en) * | 2019-12-16 | 2020-04-24 | 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 (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005091203A2 (en) * | 2004-03-12 | 2005-09-29 | Aureon Laboratories, Inc. | 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 |
| AU2019279918A1 (en) * | 2010-12-30 | 2020-01-16 | Foundation Medicine, Inc. | Optimization of multigene analysis of tumor samples |
| CN103164804A (en) * | 2011-12-16 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Personalized method and personalized device of information push |
| US9946839B1 (en) * | 2013-09-10 | 2018-04-17 | MD Insider, Inc. | Search engine systems for matching medical providers and patients |
| CN103559637A (en) * | 2013-11-13 | 2014-02-05 | 王竞 | Method and system for recommending doctor for patient |
| CN107705842A (en) * | 2017-10-13 | 2018-02-16 | 合肥工业大学 | Intelligent system for distribution of out-patient department and its method of work |
| 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 |
| 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 |
| CN108305675A (en) * | 2018-01-26 | 2018-07-20 | 合肥工业大学 | Intelligent hospital guide's method and system of diversity enhancing |
| 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 |
| WO2020046173A1 (en) * | 2018-08-27 | 2020-03-05 | Михаил Борисович БОГДАНОВ | Automated system to support medical decisions in combined pathology |
| 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 |
| 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 |
| 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 |
| CN110232971A (en) * | 2019-05-24 | 2019-09-13 | 深圳市翩翩科技有限公司 | A kind of doctor's recommended method and device |
| CN110993081A (en) * | 2019-12-03 | 2020-04-10 | 济南大学 | Doctor online recommendation method and system |
| CN111062193A (en) * | 2019-12-16 | 2020-04-24 | 医渡云（北京）技术有限公司 | Medical data labeling method and device, storage medium and electronic equipment |
| CN111009316A (en) * | 2019-12-25 | 2020-04-14 | 福州大学 | Doctor-patient matching method based on Bayesian network |
Non-Patent Citations (8)
| Title |
|---|
| MELANIE A. VAN SISE等: "Improving the selection of recommended prophylactic antibiotics using an electronic medical record", 《OBSTETRICS & GYNECOLOGY》 * |
| 刘泽田等: "基于Hadoop的医疗云存储与医生推荐系统的研究与实现", 《数字技术与应用》 * |
| 朱声荣等: "基于循证医学与真实病历库的CDSS实现与应用", 《中国数字医学》 * |
| 李勇 等: "一种混合医生推荐算法的研究", 《信息通信》 * |
| 李叶青: "在线医疗环境下多样性增强的医生推荐方法", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 * |
| 汪鹏 等: "大数据驱动的临床决策支持系统设计与实践", 《中国数字医学》 * |
| 胡德华等: "基于诊疗知识库和电子病历的整合推荐方法研究", 《中国医学物理学杂志》 * |
| 马钰等: "面对智能导诊的个性化推荐算法", 《智能系统学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496191A (en) * | 2022-02-10 | 2022-05-13 | 北京京东拓先科技有限公司 | Medical information recommendation method, device, equipment and storage medium |
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