CN112614578A - Doctor intelligent recommendation method and device, electronic equipment and storage medium - Google Patents

Doctor intelligent recommendation method and device, electronic equipment and storage medium Download PDF

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CN112614578A
CN112614578A CN202011600037.XA CN202011600037A CN112614578A CN 112614578 A CN112614578 A CN 112614578A CN 202011600037 A CN202011600037 A CN 202011600037A CN 112614578 A CN112614578 A CN 112614578A
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CN112614578B (en
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张黎明
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Shenzhen Ping An Smart Healthcare Technology Co ltd
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Ping An International Smart City Technology Co Ltd
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Abstract

The invention relates to the technical field of digital medical treatment, and provides a doctor intelligent recommendation method, a doctor intelligent recommendation device, electronic equipment and a storage medium, wherein the method comprises the following steps: extracting a plurality of original data in the historical visit information of the patient, and inputting the original data into a score identification model to obtain the recommended score of each historical visit doctor; performing regression fitting on a plurality of recommendation scores of a plurality of historical visitors and a plurality of key fields of the plurality of historical visitors; creating a first doctor model of an initial recommended doctor; acquiring a second doctor model of at least one recommended doctor in the doctor index table; and calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor to determine the target recommended doctor of the patient. According to the invention, the initial recommended doctors for creating the patient are obtained by considering the initial recommended doctors based on the dimensionality of the patient, so that the matching degree between the recommended doctors and doctors required by the patient is improved.

Description

Doctor intelligent recommendation method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of digital medical treatment, in particular to a doctor intelligent recommendation method and device, electronic equipment and a storage medium.
Background
With the increase of medical institutions and the refinement of each department, a patient can hardly make an appointment with a satisfied doctor according to the disease condition of the patient, most of the current intelligent doctor recommendation systems are performed for the doctor, and intelligent recommendation is performed for the patient by comprehensively acquiring attributes of the doctor, such as a sitting hospital, a job, a department and the like. The doctor recommended by the traditional recommendation system is often low in matching degree with the doctor required by the patient, so that the recommendation result is not accurate.
Disclosure of Invention
In view of the above, there is a need for a method, an apparatus, an electronic device and a storage medium for intelligent doctor recommendation, which improve the matching degree between a recommended doctor and a doctor required by a patient by creating an initial recommended doctor for the patient based on the dimension of the patient.
The invention provides a doctor intelligent recommendation method in a first aspect, which comprises the following steps:
acquiring historical clinic information of a patient from a plurality of preset data sources;
extracting a plurality of original data in the historical clinic information, and inputting the plurality of original data into a score recognition model to obtain a recommendation score of each historical clinic doctor, wherein the plurality of original data comprise a plurality of key fields of the plurality of historical clinic doctors and the recommendation score of each key field;
performing regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain the weight of each key field;
creating a first physician model of the initial recommended physician for the patient based on the plurality of key fields and the weight of each key field for the plurality of historical physicians;
acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient;
and calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor, and determining the target recommended doctor of the patient according to the calculated similarity.
Optionally, the performing regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain the weight of each key field includes:
performing regression fitting on the recommendation scores of the historical visitors and the key fields of the historical visitors to obtain a plurality of preset fitting functions;
and calculating the weight of each key field according to the preset fitting functions.
Optionally, the creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight corresponding to each key field includes:
selecting the doctor with the highest recommendation score as the first recommended doctor of the patient;
performing label conversion on a plurality of key fields of the first recommended doctor to obtain a label set of the first recommended doctor, wherein each label in the label set comprises a weight;
and sorting the labels in the label set in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient.
Optionally, the training process of the score recognition model includes:
acquiring a plurality of key fields of a plurality of historical doctors and a recommendation score corresponding to each key field as a sample data set;
dividing a training set and a testing set from the sample data set;
inputting the training set into a preset neural network for training to obtain a grading recognition model;
inputting the test set into the grading recognition model for testing, and calculating the test passing rate;
if the test passing rate is larger than a preset passing rate threshold value, determining that the training of the grading recognition model is finished; and if the test passing rate is smaller than the preset passing rate threshold value, increasing the number of training sets, and re-training the score recognition model.
Optionally, the obtaining a second doctor model of at least one recommended doctor in the doctor index table according to the historical visit information of the patient includes:
extracting a plurality of disease information from the patient's historical visit information;
performing word segmentation processing on the plurality of disease information to obtain a plurality of disease characteristic attributes;
performing label conversion on the plurality of disease characteristic attributes to obtain a label set of the patient;
and finding out at least one recommended doctor matched with the label set in a doctor index table, and associating a second doctor model of the at least one recommended doctor from the doctor index table.
Optionally, the calculating the similarity between the first doctor model and the second doctor model of each recommended doctor comprises:
extracting all first labels in the first doctor model, and calculating the total number of the first labels of all the first labels;
extracting all second labels in a second doctor model of each recommended doctor;
searching all target tags matched with all the first tags from all the second tags, and calculating the total number of the second tags of all the target tags;
and taking the quotient of the second label total number and the first label total number as the similarity between the first doctor model and the second doctor model of each recommended doctor.
Optionally, the determining the target recommended doctor of the patient according to the calculated similarity includes:
the similarity between the first doctor model obtained through calculation and the second doctor model of each recommended doctor is sorted in a descending order;
selecting a plurality of second recommended doctors corresponding to a plurality of similarity degrees ranked in the front from the result of descending order ranking;
determining the patient's preferences from the patient's historical encounter information;
and determining a target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient.
A second aspect of the present invention provides a doctor intelligent recommendation apparatus, including:
the acquisition module is used for acquiring historical clinic information of the patient from a plurality of preset data sources;
the extraction module is used for extracting a plurality of original data in the historical visit information and inputting the original data into a score identification model to obtain the recommendation score of each historical visit doctor, wherein the original data comprises a plurality of key fields of the historical visit doctors and the recommendation score of each key field;
the regression fitting module is used for carrying out regression fitting on the recommendation scores of the historical doctors and the key fields of the historical doctors to obtain the weight of each key field;
a creating module for creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight of each key field;
the acquisition module is used for acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient;
and the calculation module is used for calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor and determining the target recommended doctor of the patient according to the calculated similarity.
A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being adapted to implement the method for intelligent recommendation by a physician when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for intelligent recommendation by a physician.
In summary, according to the method, the apparatus, the electronic device and the storage medium for intelligent recommendation of doctors described in the present invention, on one hand, a first doctor model of an initial recommended doctor of the patient is created according to a plurality of key fields of a plurality of historical doctors and a weight corresponding to each key field, and since the initial recommended doctor is obtained by considering based on the dimensionality of the patient, the matching degree between the recommended doctor and a doctor required by the patient is improved; on the other hand, the similarity between the first doctor model and the second doctor model of each recommended doctor is calculated, the target recommended doctor of the patient is determined according to the calculated similarity, the target recommended doctor of the patient is obtained by considering the dimensionality of the patient and the dimensionality of the doctor at the same time, the matching degree between the recommended doctor and the doctor required by the patient is improved, and the accuracy of the target recommended doctor is further improved; finally, regression fitting is carried out on the plurality of recommendation scores of the plurality of historical doctor visits and the plurality of key fields to obtain the weight corresponding to each key field, the plurality of key fields are set according to the requirements of the patient, and the matching degree between the recommended doctor and the doctor required by the patient can be improved by calculating the weight corresponding to each key field.
Drawings
Fig. 1 is a flowchart of a method for intelligent recommendation of a doctor according to an embodiment of the present invention.
Fig. 2 is a diagram for obtaining a plurality of recommendation scores and a plurality of key fields of a plurality of historical visitors according to an embodiment of the present invention.
Fig. 3 is a structural diagram of a doctor intelligent recommendation apparatus according to a second embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example one
Fig. 1 is a flowchart of a method for intelligent recommendation of a doctor according to an embodiment of the present invention.
In this embodiment, the method for intelligently recommending doctors can be applied to electronic devices, and for electronic devices that need to make intelligent doctor recommendations, the function provided by the method for intelligently recommending doctors can be directly integrated on the electronic devices, or the function can be run in the electronic devices in the form of Software Development Kit (SKD).
As shown in FIG. 1, the method for intelligent recommendation of doctors specifically includes the following steps, and the order of the steps in the flowchart can be changed and some steps can be omitted according to different requirements.
And S11, acquiring historical clinic information of the patient from a plurality of preset data sources.
In this embodiment, a plurality of data sources may be preset, where the data sources may be a plurality of diagnosis platforms associated with the historical diagnosis information of each patient, and crawl the historical diagnosis information of each patient from each preset data source, or receive the historical diagnosis information reported by the preset data sources, and specifically, the historical diagnosis information includes: historical disease information for the patient and physician information for the historical visit physician.
In other embodiments, the historical visit information may be a text file, a video file, or a voice file, and if the historical visit information is a video file, voice information may be collected from the video file, and the voice information is converted into a text file by using a voice recognition technology; and if the historical visit information is a voice file, converting the voice file into a text file by adopting a voice recognition technology.
And S12, extracting a plurality of raw data in the historical visit information, and inputting the raw data into a score recognition model to obtain the recommendation score of each historical visit doctor, wherein the raw data comprises a plurality of key fields of the historical visit doctors and the recommendation score of each key field.
In this embodiment, the plurality of raw data are extracted from historical clinic information, specifically, the plurality of raw data are a plurality of key fields of a plurality of historical doctors in the historical clinic information and a recommendation score of each key field of each historical doctor, and the plurality of key fields may be set in advance according to a requirement of a patient, for example, the plurality of key fields may include: the scoring identification model can be trained in advance, and after a plurality of key fields of the plurality of historical doctors are obtained, the plurality of key fields of the plurality of historical doctors are input into the pre-trained scoring identification model to obtain the recommendation score of each historical doctor.
Specifically, the training process of the score recognition model comprises the following steps:
21) acquiring a plurality of key fields of a plurality of historical doctors and a recommendation score corresponding to each key field as a sample data set;
22) dividing a training set and a testing set from the sample data set;
23) inputting the training set into a preset neural network for training to obtain a grading recognition model;
24) inputting the test set into the grading recognition model for testing, and calculating the test passing rate;
25) if the test passing rate is larger than a preset passing rate threshold value, determining that the training of the grading recognition model is finished; and if the test passing rate is smaller than the preset passing rate threshold value, increasing the number of training sets, and re-training the score recognition model.
In this embodiment, the scoring recognition model is obtained by training according to different recommendation scores corresponding to each key field of different doctors, the plurality of key fields of the plurality of historical doctors and the recommendation score of each key field of each historical doctor are input into the pre-trained scoring recognition model for recognition to obtain the recommendation score of each historical doctor, and in the subsequent training process, the plurality of key fields of the historical doctor corresponding to each patient and the recommendation score of each key field of each historical doctor are used as new data to increase the number of the data sets, the scoring recognition model is retrained based on the new data sets, and the scoring recognition model is continuously updated, so that the recognition rate is continuously improved.
And S13, performing regression fitting on the recommendation scores of the historical visitors and the key fields of the historical visitors to obtain the weight of each key field.
In this embodiment, a plurality of key fields of each historical doctor are consistent, and preferably, the weight corresponding to each key field may be calculated by a regression fitting algorithm.
Optionally, the performing regression fitting on the plurality of recommendation scores of the plurality of historical physicians and the plurality of key fields of the plurality of historical physicians to obtain the weight of each key field comprises:
performing regression fitting on the recommendation scores of the historical visitors and the key fields of the historical visitors to obtain a plurality of preset fitting functions;
and calculating to obtain the weight corresponding to each key field according to the preset fitting functions.
Illustratively, a plurality of recommendation scores and the plurality of key fields for a plurality of historical physicians are obtained, as shown in fig. 2: the method comprises the steps of carrying out regression fitting on each historical doctor according to a preset fitting function to obtain a preset fitting function, carrying out regression fitting on all historical doctors to obtain a plurality of preset fitting functions, and further calculating to obtain the weight corresponding to each key field according to the preset fitting functions.
For example, if the calculated weight of the job title of the historical doctor for the patient is the largest, determining that the most important patient is the job title of the doctor; and if the calculated weight of the sex of the historical doctor for the patient is the maximum, determining that the patient who has the greatest weight is the sex of the doctor.
In this embodiment, since the plurality of key fields are set according to the requirements of the patient, the matching degree between the recommended doctor and the doctor required by the patient can be improved by calculating the weight corresponding to each key field.
S14, a first doctor model of the initial recommended doctor of the patient is created according to the plurality of key fields of the plurality of historical visitors and the weight of each key field.
In this embodiment, the first doctor model is an initial recommended doctor created according to a plurality of key fields of the historical doctor during the historical visit of the patient and a weight corresponding to each key field, and specifically, the initial recommended doctor is created by considering the dimension of the patient.
Optionally, the creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight corresponding to each key field comprises:
selecting the doctor with the highest recommendation score as the first recommended doctor of the patient;
performing label conversion on a plurality of key fields of the first recommended doctor to obtain a label set of the first recommended doctor, wherein each label in the label set comprises a weight;
and sorting the labels in the label set in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient.
In this embodiment, according to the score of each historical doctor for the patient, the doctor with the highest recommended score is selected as the first recommended doctor of the patient, and label conversion is performed on a plurality of key fields corresponding to the first recommended doctor according to a preset label conversion rule to obtain a label set of the first recommended doctor, for example: aiming at the key field age corresponding to the first recommended doctor B, according to a preset conversion rule of the age: 26-35, Zhuang-year; 36-45, full year; 46-55, daryely, 56-65, middle-aged; 66-75 and old age. If the age of the first recommended doctor B is 29 years old, determining that the label corresponding to the age of the first recommended doctor is as follows: it is good for the old.
After the label set of the first recommended doctor is obtained, the labels in the label set are sorted in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient, and the initial recommended doctor is obtained by considering the dimension of the patient, so that the matching degree between the recommended doctor and a doctor required by the patient is improved.
And S15, acquiring a second doctor model of at least one recommended doctor in the doctor index table according to the historical clinic information of the patient.
In this embodiment, because the second doctor model of each recommended doctor is associated in the doctor index table, and the second doctor model includes a plurality of tags, at least one recommended doctor can be quickly determined according to the historical visit information of the patient, and the second doctor model of the recommended doctor is associated, so that the efficiency of determining the recommended doctor is improved.
Optionally, the obtaining a second doctor model of at least one recommended doctor in a doctor index table according to the historical visit information of the patient includes:
extracting a plurality of disease information from the patient's historical visit information;
performing word segmentation processing on the plurality of disease information to obtain a plurality of disease characteristic attributes;
performing label conversion on the plurality of disease characteristic attributes to obtain a label set of the patient;
and finding out at least one recommended doctor matched with the label set in a doctor index table, and associating a second doctor model of the at least one recommended doctor from the doctor index table.
In this embodiment, the physician index table includes a second physician model of each recommended physician, where the second physician model includes a plurality of labels, and the areas of excellence, age, sex, and physician title of the historical physicians can be determined by the plurality of labels.
In this embodiment, before the obtaining of the doctor model of at least one recommended doctor in the doctor index table according to the historical visit information of the patient, the doctor index table needs to be created, specifically, the creating process of the doctor index table includes: adopt web crawler technology to crawl a plurality of doctor information of seeing a doctor from a plurality of predetermined target data sources, extract a plurality of predetermined doctor characteristic attribute from every doctor information of seeing a doctor, will a plurality of predetermined doctor characteristic attribute convert target label set into according to predetermined target label conversion rule, will target label set is as every doctor model of seeing a doctor, because target label set obtains through machine learning, will target label set is as every doctor model of seeing a doctor has improved the rate of accuracy of the second doctor model that obtains.
S16, calculating the similarity between the first doctor model and the second doctor model of the at least one recommended doctor, and determining the target recommended doctor of the patient according to the calculated similarity.
In this embodiment, the target recommended doctor is determined by similarity between the first doctor model of the initial recommended doctor of the patient and the second doctor model of each recommended doctor recommended by the system.
Optionally, the calculating the similarity between the first doctor model and the second doctor model of each recommended doctor comprises:
extracting all first labels in the first doctor model, and calculating the total number of the first labels of all the first labels;
extracting all second labels in a second doctor model of each recommended doctor;
searching all target tags matched with all the first tags from all the second tags, and calculating the total number of the second tags of all the target tags;
and taking the quotient of the second label total number and the first label total number as the similarity between the first doctor model and the second doctor model of each recommended doctor.
In this embodiment, the first doctor model includes a plurality of first tags, the second doctor model includes a plurality of second tags, each first tag is matched with the plurality of second tags, all target tags matched with the first tags are found, and a quotient of a total number of the second tags and a total number of the first tags of all the target tags is calculated as a similarity between the first doctor model and the second doctor model of each recommended doctor.
Optionally, the determining the target recommended doctor of the patient according to the calculated similarity includes:
the similarity between the first doctor model obtained through calculation and the second doctor model of each recommended doctor is sorted in a descending order;
selecting a plurality of second recommended doctors corresponding to a plurality of similarity degrees ranked in the front from the result of descending order ranking;
determining the patient's preferences from the patient's historical encounter information;
and determining a target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient.
In this embodiment, a collaborative filtering recommendation algorithm may be adopted to determine the target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient, in this embodiment, the target recommended doctor of the patient is determined according to the similarity by calculating the similarity between the second doctor model recommended by the system and the first doctor recommendation model required by the patient, and the target recommended doctor of the patient is obtained by considering the dimensions of the patient and the doctor at the same time, so that the matching degree between the recommended doctor and the doctor required by the patient is improved, and the accuracy of the target recommended doctor is further improved.
In summary, in the intelligent doctor recommendation method according to the embodiment, historical visit information of a patient is collected from a plurality of preset data sources; extracting a plurality of original data in the historical clinic information, and inputting the plurality of original data into a score recognition model to obtain a recommendation score of each historical clinic doctor, wherein the plurality of original data comprise a plurality of key fields of the plurality of historical clinic doctors and the recommendation score of each key field; performing regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain the weight of each key field; creating a first physician model of the initial recommended physician for the patient based on the plurality of key fields and the weight of each key field for the plurality of historical physicians; acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient; and calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor, and determining the target recommended doctor of the patient according to the calculated similarity.
In this embodiment, on one hand, a first doctor model of an initial recommended doctor of the patient is created according to a plurality of key fields of the plurality of historical visitors and the weight corresponding to each key field, and since the initial recommended doctor is obtained by considering based on the dimensionality of the patient, the matching degree between the recommended doctor and a doctor required by the patient is improved; on the other hand, the similarity between the first doctor model and the second doctor model of each recommended doctor is calculated, the target recommended doctor of the patient is determined according to the calculated similarity, the target recommended doctor of the patient is obtained by considering the dimensionality of the patient and the dimensionality of the doctor at the same time, the matching degree between the recommended doctor and the doctor required by the patient is improved, and the accuracy of the target recommended doctor is further improved; finally, regression fitting is carried out on the plurality of recommendation scores of the plurality of historical doctor visits and the plurality of key fields to obtain the weight corresponding to each key field, the plurality of key fields are set according to the requirements of the patient, and the matching degree between the recommended doctor and the doctor required by the patient can be improved by calculating the weight corresponding to each key field.
Example two
Fig. 3 is a structural diagram of a doctor intelligent recommendation apparatus according to a second embodiment of the present invention.
In some embodiments, the intelligent recommendation device 20 may include a plurality of functional modules composed of program code segments. The program codes of the various program segments in the intelligent recommendation device 20 for doctors can be stored in the memory of the electronic equipment and executed by the at least one processor to execute the functions (detailed in fig. 1) intelligently recommended by doctors.
In this embodiment, the intelligent recommendation device 20 may be divided into a plurality of functional modules according to the functions performed by the intelligent recommendation device. The functional module may include: an acquisition module 201, an extraction module 202, a regression fitting module 203, a creation module 204, an acquisition module 205, and a calculation module 206. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The acquisition module 201 is configured to acquire historical visit information of a patient from a plurality of preset data sources.
In this embodiment, a plurality of data sources may be preset, where the data sources may be a plurality of diagnosis platforms associated with the historical diagnosis information of each patient, and crawl the historical diagnosis information of each patient from each preset data source, or receive the historical diagnosis information reported by the preset data sources, and specifically, the historical diagnosis information includes: historical disease information for the patient and physician information for the historical visit physician.
In other embodiments, the historical visit information may be a text file, a video file, or a voice file, and if the historical visit information is a video file, voice information may be collected from the video file, and the voice information is converted into a text file by using a voice recognition technology; and if the historical visit information is a voice file, converting the voice file into a text file by adopting a voice recognition technology.
The extracting module 202 is configured to extract a plurality of raw data in the historical visiting information, and input the plurality of raw data into a score recognition model to obtain a recommendation score of each historical visiting physician, where the plurality of raw data includes a plurality of key fields of the plurality of historical visiting physicians and a recommendation score of each key field.
In this embodiment, the plurality of raw data are extracted from historical clinic information, specifically, the plurality of raw data are a plurality of key fields of a plurality of historical doctors in the historical clinic information and a recommendation score of each key field of each historical doctor, and the plurality of key fields may be set in advance according to a requirement of a patient, for example, the plurality of key fields may include: the scoring identification model can be trained in advance, and after a plurality of key fields of the plurality of historical doctors are obtained, the plurality of key fields of the plurality of historical doctors are input into the pre-trained scoring identification model to obtain the recommendation score of each historical doctor.
Specifically, the training process of the score recognition model comprises the following steps:
21) acquiring a plurality of key fields of a plurality of historical doctors and a recommendation score corresponding to each key field as a sample data set;
22) dividing a training set and a testing set from the sample data set;
23) inputting the training set into a preset neural network for training to obtain a grading recognition model;
24) inputting the test set into the grading recognition model for testing, and calculating the test passing rate;
25) if the test passing rate is larger than a preset passing rate threshold value, determining that the training of the grading recognition model is finished; and if the test passing rate is smaller than the preset passing rate threshold value, increasing the number of training sets, and re-training the score recognition model.
In this embodiment, the scoring recognition model is obtained by training according to different recommendation scores corresponding to each key field of different doctors, the plurality of key fields of the plurality of historical doctors and the recommendation score of each key field of each historical doctor are input into the pre-trained scoring recognition model for recognition to obtain the recommendation score of each historical doctor, and in the subsequent training process, the plurality of key fields of the historical doctor corresponding to each patient and the recommendation score of each key field of each historical doctor are used as new data to increase the number of the data sets, the scoring recognition model is retrained based on the new data sets, and the scoring recognition model is continuously updated, so that the recognition rate is continuously improved.
A regression fitting module 203, configured to perform regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain a weight of each key field.
In this embodiment, a plurality of key fields of each historical doctor are consistent, and preferably, the weight corresponding to each key field may be calculated by a regression fitting algorithm.
Optionally, the regression fitting module 203 performing regression fitting on the plurality of recommendation scores of the plurality of historical physicians and the plurality of key fields of the plurality of historical physicians to obtain the weight of each key field includes:
performing regression fitting on the recommendation scores of the historical visitors and the key fields of the historical visitors to obtain a plurality of preset fitting functions;
and calculating to obtain the weight corresponding to each key field according to the preset fitting functions.
Illustratively, a plurality of recommendation scores and the plurality of key fields for a plurality of historical physicians are obtained, as shown in fig. 2: the method comprises the steps of carrying out regression fitting on each historical doctor according to a preset fitting function to obtain a preset fitting function, carrying out regression fitting on all historical doctors to obtain a plurality of preset fitting functions, and further calculating to obtain the weight corresponding to each key field according to the preset fitting functions.
For example, if the calculated weight of the job title of the historical doctor for the patient is the largest, determining that the most important patient is the job title of the doctor; and if the calculated weight of the sex of the historical doctor for the patient is the maximum, determining that the patient who has the greatest weight is the sex of the doctor.
In this embodiment, since the plurality of key fields are set according to the requirements of the patient, the matching degree between the recommended doctor and the doctor required by the patient can be improved by calculating the weight corresponding to each key field.
A creating module 204 for creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight of each key field.
In this embodiment, the first doctor model is an initial recommended doctor created according to a plurality of key fields of the historical doctor during the historical visit of the patient and a weight corresponding to each key field, and specifically, the initial recommended doctor is created by considering the dimension of the patient.
Optionally, the creating module 204 creating the first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight corresponding to each key field includes:
selecting the doctor with the highest recommendation score as the first recommended doctor of the patient;
performing label conversion on a plurality of key fields of the first recommended doctor to obtain a label set of the first recommended doctor, wherein each label in the label set comprises a weight;
and sorting the labels in the label set in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient.
In this embodiment, according to the score of each historical doctor for the patient, the doctor with the highest recommended score is selected as the first recommended doctor of the patient, and label conversion is performed on a plurality of key fields corresponding to the first recommended doctor according to a preset label conversion rule to obtain a label set of the first recommended doctor, for example: aiming at the key field age corresponding to the first recommended doctor B, according to a preset conversion rule of the age: 26-35, Zhuang-year; 36-45, full year; 46-55, daryely, 56-65, middle-aged; 66-75 and old age. If the age of the first recommended doctor B is 29 years old, determining that the label corresponding to the age of the first recommended doctor is as follows: it is good for the old.
After the label set of the first recommended doctor is obtained, the labels in the label set are sorted in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient, and the initial recommended doctor is obtained by considering the dimension of the patient, so that the matching degree between the recommended doctor and a doctor required by the patient is improved.
An obtaining module 205, configured to obtain a second doctor model of at least one recommended doctor in the doctor index table according to the historical visit information of the patient.
In this embodiment, because the second doctor model of each recommended doctor is associated in the doctor index table, and the second doctor model includes a plurality of tags, at least one recommended doctor can be quickly determined according to the historical visit information of the patient, and the second doctor model of the recommended doctor is associated, so that the efficiency of determining the recommended doctor is improved.
Optionally, the obtaining module 205 obtains a second doctor model of at least one recommended doctor in the doctor index table according to the historical visit information of the patient includes:
extracting a plurality of disease information from the patient's historical visit information;
performing word segmentation processing on the plurality of disease information to obtain a plurality of disease characteristic attributes;
performing label conversion on the plurality of disease characteristic attributes to obtain a label set of the patient;
and finding out at least one recommended doctor matched with the label set in a doctor index table, and associating a second doctor model of the at least one recommended doctor from the doctor index table.
In this embodiment, the physician index table includes a second physician model of each recommended physician, where the second physician model includes a plurality of labels, and the areas of excellence, age, sex, and physician title of the historical physicians can be determined by the plurality of labels.
In this embodiment, before the obtaining of the doctor model of at least one recommended doctor in the doctor index table according to the historical visit information of the patient, the doctor index table needs to be created, specifically, the creating process of the doctor index table includes: adopt web crawler technology to crawl a plurality of doctor information of seeing a doctor from a plurality of predetermined target data sources, extract a plurality of predetermined doctor characteristic attribute from every doctor information of seeing a doctor, will a plurality of predetermined doctor characteristic attribute convert target label set into according to predetermined target label conversion rule, will target label set is as every doctor model of seeing a doctor, because target label set obtains through machine learning, will target label set is as every doctor model of seeing a doctor has improved the rate of accuracy of the second doctor model that obtains.
A calculating module 206, configured to calculate a similarity between the first doctor model and a second doctor model of the at least one recommended doctor, and determine a target recommended doctor of the patient according to the calculated similarity.
In this embodiment, the target recommended doctor is determined by similarity between the first doctor model of the initial recommended doctor of the patient and the second doctor model of each recommended doctor recommended by the system.
Optionally, the calculating module 206 calculating the similarity between the first doctor model and the second doctor model of each recommended doctor comprises:
extracting all first labels in the first doctor model, and calculating the total number of the first labels of all the first labels;
extracting all second labels in a second doctor model of each recommended doctor;
searching all target tags matched with all the first tags from all the second tags, and calculating the total number of the second tags of all the target tags;
and taking the quotient of the second label total number and the first label total number as the similarity between the first doctor model and the second doctor model of each recommended doctor.
In this embodiment, the first doctor model includes a plurality of first tags, the second doctor model includes a plurality of second tags, each first tag is matched with the plurality of second tags, all target tags matched with the first tags are found, and a quotient of a total number of the second tags and a total number of the first tags of all the target tags is calculated as a similarity between the first doctor model and the second doctor model of each recommended doctor.
Optionally, the determining, by the calculation module 206, the target recommended doctor of the patient according to the calculated similarity includes:
the similarity between the first doctor model obtained through calculation and the second doctor model of each recommended doctor is sorted in a descending order;
selecting a plurality of second recommended doctors corresponding to a plurality of similarity degrees ranked in the front from the result of descending order ranking;
determining the patient's preferences from the patient's historical encounter information;
and determining a target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient.
In this embodiment, a collaborative filtering recommendation algorithm may be adopted to determine the target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient, in this embodiment, the target recommended doctor of the patient is determined according to the similarity by calculating the similarity between the second doctor model recommended by the system and the first doctor recommendation model required by the patient, and the target recommended doctor of the patient is obtained by considering the dimensions of the patient and the doctor at the same time, so that the matching degree between the recommended doctor and the doctor required by the patient is improved, and the accuracy of the target recommended doctor is further improved.
In summary, the intelligent doctor recommendation device according to the embodiment collects historical clinic information of a patient from a plurality of preset data sources; extracting a plurality of original data in the historical clinic information, and inputting the plurality of original data into a score recognition model to obtain a recommendation score of each historical clinic doctor, wherein the plurality of original data comprise a plurality of key fields of the plurality of historical clinic doctors and the recommendation score of each key field; performing regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain the weight of each key field; creating a first physician model of the initial recommended physician for the patient based on the plurality of key fields and the weight of each key field for the plurality of historical physicians; acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient; and calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor, and determining the target recommended doctor of the patient according to the calculated similarity.
In this embodiment, on one hand, a first doctor model of an initial recommended doctor of the patient is created according to a plurality of key fields of the plurality of historical visitors and the weight corresponding to each key field, and since the initial recommended doctor is obtained by considering based on the dimensionality of the patient, the matching degree between the recommended doctor and a doctor required by the patient is improved; on the other hand, the similarity between the first doctor model and the second doctor model of each recommended doctor is calculated, the target recommended doctor of the patient is determined according to the calculated similarity, the target recommended doctor of the patient is obtained by considering the dimensionality of the patient and the dimensionality of the doctor at the same time, the matching degree between the recommended doctor and the doctor required by the patient is improved, and the accuracy of the target recommended doctor is further improved; finally, regression fitting is carried out on the plurality of recommendation scores of the plurality of historical doctor visits and the plurality of key fields to obtain the weight corresponding to each key field, the plurality of key fields are set according to the requirements of the patient, and the matching degree between the recommended doctor and the doctor required by the patient can be improved by calculating the weight corresponding to each key field.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 4 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less hardware or software than those shown, or different component arrangements.
In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.
It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
In some embodiments, the memory 31 is used for storing program codes and various data, such as the doctor's intelligent recommendation device 20 installed in the electronic device 3, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by using various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
In a further embodiment, in conjunction with fig. 3, the at least one processor 32 may execute an operating device of the electronic device 3 and various installed application programs (such as the doctor intelligent recommendation device 20), program codes, and the like, for example, the above modules.
The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules illustrated in fig. 3 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of intelligent recommendation by doctors.
In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 to implement the functions intelligently recommended by the physician.
Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (10)

1. A doctor intelligent recommendation method is characterized by comprising the following steps:
acquiring historical clinic information of a patient from a plurality of preset data sources;
extracting a plurality of original data in the historical clinic information, and inputting the plurality of original data into a score recognition model to obtain a recommendation score of each historical clinic doctor, wherein the plurality of original data comprise a plurality of key fields of the plurality of historical clinic doctors and the recommendation score of each key field;
performing regression fitting on the plurality of recommendation scores of the plurality of historical visitors and the plurality of key fields of the plurality of historical visitors to obtain the weight of each key field;
creating a first physician model of the initial recommended physician for the patient based on the plurality of key fields and the weight of each key field for the plurality of historical physicians;
acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient;
and calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor, and determining the target recommended doctor of the patient according to the calculated similarity.
2. The method of claim 1, wherein the regression fitting the plurality of recommendation scores for the plurality of historical physicians and the plurality of key fields for the plurality of historical physicians to obtain the weight for each key field comprises:
performing regression fitting on the recommendation scores of the historical visitors and the key fields of the historical visitors to obtain a plurality of preset fitting functions;
and calculating the weight of each key field according to the preset fitting functions.
3. The intelligent doctor recommendation method according to claim 1, wherein the creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical physicians and the weight corresponding to each key field comprises:
selecting the doctor with the highest recommendation score as the first recommended doctor of the patient;
performing label conversion on a plurality of key fields of the first recommended doctor to obtain a label set of the first recommended doctor, wherein each label in the label set comprises a weight;
and sorting the labels in the label set in a descending order according to the weight to obtain a first doctor model of the initial recommended doctor of the patient.
4. The intelligent recommendation method for doctors as claimed in claim 1, wherein the training process of the score recognition model comprises:
acquiring a plurality of key fields of a plurality of historical doctors and a recommendation score corresponding to each key field as a sample data set;
dividing a training set and a testing set from the sample data set;
inputting the training set into a preset neural network for training to obtain a grading recognition model;
inputting the test set into the grading recognition model for testing, and calculating the test passing rate;
if the test passing rate is larger than a preset passing rate threshold value, determining that the training of the grading recognition model is finished; and if the test passing rate is smaller than the preset passing rate threshold value, increasing the number of training sets, and re-training the score recognition model.
5. The intelligent doctor recommendation method according to claim 1, wherein said obtaining a second doctor model of at least one recommended doctor in a doctor index table according to the patient's historical visit information comprises:
extracting a plurality of disease information from the patient's historical visit information;
performing word segmentation processing on the plurality of disease information to obtain a plurality of disease characteristic attributes;
performing label conversion on the plurality of disease characteristic attributes to obtain a label set of the patient;
and finding out at least one recommended doctor matched with the label set in a doctor index table, and associating a second doctor model of the at least one recommended doctor from the doctor index table.
6. The intelligent doctor recommendation method according to claim 1, wherein said calculating a similarity between the first doctor model and the second doctor model of each recommending doctor comprises:
extracting all first labels in the first doctor model, and calculating the total number of the first labels of all the first labels;
extracting all second labels in a second doctor model of each recommended doctor;
searching all target tags matched with all the first tags from all the second tags, and calculating the total number of the second tags of all the target tags;
and taking the quotient of the second label total number and the first label total number as the similarity between the first doctor model and the second doctor model of each recommended doctor.
7. The intelligent doctor recommendation method according to claim 6, wherein said determining the target recommended doctor of the patient based on the calculated similarity comprises:
the similarity between the first doctor model obtained through calculation and the second doctor model of each recommended doctor is sorted in a descending order;
selecting a plurality of second recommended doctors corresponding to a plurality of similarity degrees ranked in the front from the result of descending order ranking;
determining the patient's preferences from the patient's historical encounter information;
and determining a target recommended doctor of the patient from the plurality of second recommended doctors according to the preference of the patient.
8. A physician intelligent recommendation apparatus, the apparatus comprising:
the acquisition module is used for acquiring historical clinic information of the patient from a plurality of preset data sources;
the extraction module is used for extracting a plurality of original data in the historical visit information and inputting the original data into a score identification model to obtain the recommendation score of each historical visit doctor, wherein the original data comprises a plurality of key fields of the historical visit doctors and the recommendation score of each key field;
the regression fitting module is used for carrying out regression fitting on the recommendation scores of the historical doctors and the key fields of the historical doctors to obtain the weight of each key field;
a creating module for creating a first doctor model of the initial recommended doctor of the patient according to the plurality of key fields of the plurality of historical visitors and the weight of each key field;
the acquisition module is used for acquiring a second doctor model of at least one recommended doctor in a doctor index table according to the historical clinic information of the patient;
and the calculation module is used for calculating the similarity between the first doctor model and a second doctor model of the at least one recommended doctor and determining the target recommended doctor of the patient according to the calculated similarity.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to implement the method of physician intelligent recommendation of any of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for intelligent recommendation by a physician as claimed in any one of claims 1 to 7.
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