CN113658712A - Doctor-patient matching method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses a doctor-patient matching method, a doctor-patient matching device, doctor-patient matching equipment and a storage medium, which are used for improving the accuracy of doctor-patient matching. The doctor-patient matching method comprises the following steps: obtaining symptom data of a patient, the symptom data including a plurality of symptom names; performing medical entity recognition on the symptom data through a plurality of deep learning models to obtain a patient medical entity set, wherein the plurality of deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model; acquiring a plurality of candidate doctors in a candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result; and ranking a plurality of candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient. In addition, the invention also relates to a block chain technology, and the matching result can be stored in the block chain node.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a doctor-patient matching method, a doctor-patient matching device, doctor-patient matching equipment and a storage medium.
Background
With the increasing frequency of the internet in the life of people, the depth and the breadth of the internet are greatly improved, and the integration development of the internet and medical health is inevitable. The Internet hospitals are rapidly built, and the development acceleration trend is obvious. But when using an internet hospital, it is a frequent scenario how to find a doctor suitable for the patient. At present, the industry adopts a list classified tour to show the profession and specialties of doctors to patients, and the patients can select the professions and specialties. Patients face so many choices that they often face selection difficulties.
At present, doctors and patients are matched with doctors suitable for the patients when the patients use internet hospitals, the existing scheme is that the professions and specialties of the doctors are presented to the patients through list classified tour, and the doctors and the patients are matched according to the preference of the patients, but the accuracy rate of the doctors and the patients is low at present.
Disclosure of Invention
The invention provides a doctor-patient matching method, a doctor-patient matching device, doctor-patient matching equipment and a storage medium, which are used for improving the accuracy of doctor-patient matching.
The invention provides a doctor-patient matching method in a first aspect, which comprises the following steps: acquiring symptom data of a patient, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient; medical entity recognition is carried out on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, wherein the deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model; acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating a set corresponding to symptoms which are treated by the candidate doctors well; and ranking the candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
Optionally, in a first implementation manner of the first aspect of the present invention, before the obtaining symptom data of the patient, where the symptom data is used to indicate a plurality of symptom names corresponding to the patient, the doctor-patient matching method further includes: acquiring doctor registration information of the candidate doctors, and inputting the doctor registration information into a preset entity identification model for entity identification to obtain a registration information entity; obtaining historical inquiry records of the candidate doctors, extracting favorable evaluation records from the historical inquiry records, and performing entity analysis on the favorable evaluation records to obtain favorable evaluation record entities; and generating the doctor attribute entity set according to the registration information entity and the favorable comment recording entity.
Optionally, in a second implementation manner of the first aspect of the present invention, the performing medical entity identification on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, where the plurality of deep learning models include an entity identification model, a disease prediction model, and a relationship entity model, and include: carrying out entity recognition on the symptom data through a preset entity recognition model to obtain a medical entity; carrying out disease identification on the symptom data through a preset disease prediction model to obtain a disease entity; performing associated entity identification on the symptom data through a preset relationship entity model to obtain an associated medical entity; determining the medical entity, the disease entity, and the associated medical entity as the set of patient medical entities.
Optionally, in a third implementation manner of the first aspect of the present invention, after obtaining symptom data of a patient, where the symptom data is used to indicate a plurality of symptom names corresponding to the patient, the medical entity recognition is performed on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, where the plurality of deep learning models include an entity recognition model, a disease prediction model, and a relationship entity model, and before the obtaining of the symptom data of the patient, the doctor-patient matching method further includes: acquiring a target entity subset in a preset entity expansion library; performing entity matching on the target entity subset and the patient medical entity set to obtain a medical extended entity; and performing associated storage on the medical extension entity and the patient medical entity set.
Optionally, in a fourth implementation manner of the first aspect of the present invention, before the obtaining the target entity subset in the preset entity expansion library, the doctor-patient matching method further includes: calling a preset medical article corpus set, and acquiring a first-class entity subset from the medical article corpus set, wherein the first-class entity subset is used for indicating a plurality of entities with the probability of occurrence being larger than a preset threshold value in the same article in the medical article corpus set; calling a preset knowledge graph, and extracting a second entity subset from the knowledge graph, wherein the second entity subset is used for indicating a plurality of disease entities corresponding to the disease conditions in the knowledge graph; determining the first type entity subset and the second type entity subset as entity extension libraries.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the obtaining multiple candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the multiple candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, where the doctor attribute entity set is used to indicate that the multiple candidate doctors are good at a set corresponding to a treated medical condition, and the method includes: acquiring a plurality of candidate doctors in a preset candidate doctor set, and associating the candidate doctors with doctor attribute entities corresponding to each candidate doctor to obtain a doctor attribute entity set of the candidate doctors; calculating entity correlation degree of the doctor attribute entity set and the patient medical entity set to obtain target correlation degree; and comparing a preset target value with the target relevancy, and taking a plurality of corresponding candidate doctors as matching results when the target relevancy is greater than the preset target value.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the ranking the candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient includes acquiring preference data of a user, and performing descending ranking on the candidate doctors in the candidate doctor set corresponding to the matching result according to the preference data to obtain a target ranking; and taking the candidate doctor with the highest ranking in the target ranking as the doctor matched with the patient.
A second aspect of the present invention provides a doctor-patient matching apparatus, including: the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring symptom data of a patient, and the symptom data is used for indicating a plurality of symptom names corresponding to the patient; the processing module is used for carrying out medical entity recognition on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model; the matching module is used for acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating a set corresponding to symptoms which are treated by the candidate doctors; and the generating module is used for ranking the candidate doctors in the candidate doctor set according to the matching result and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
Optionally, in a first implementation manner of the second aspect of the present invention, the doctor-patient matching apparatus further includes: the configuration module is used for acquiring doctor registration information of the candidate doctors and inputting the doctor registration information into a preset entity identification model for entity identification to obtain a registration information entity; obtaining historical inquiry records of the candidate doctors, extracting favorable evaluation records from the historical inquiry records, and performing entity analysis on the favorable evaluation records to obtain favorable evaluation record entities; and generating the doctor attribute entity set according to the registration information entity and the favorable comment recording entity.
Optionally, in a second implementation manner of the second aspect of the present invention, the processing module is specifically configured to: carrying out entity recognition on the symptom data through a preset entity recognition model to obtain a medical entity; carrying out disease identification on the symptom data through a preset disease prediction model to obtain a disease entity; performing associated entity identification on the symptom data through a preset relationship entity model to obtain an associated medical entity; determining the medical entity, the disease entity, and the associated medical entity as the set of patient medical entities.
Optionally, in a third implementation manner of the second aspect of the present invention, the doctor-patient matching apparatus further includes: the storage module is used for acquiring a target entity subset in a preset entity extension library; performing entity matching on the target entity subset and the patient medical entity set to obtain a medical extended entity; and performing associated storage on the medical extension entity and the patient medical entity set.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the doctor-patient matching apparatus further includes: the system comprises an expansion module, a search module and a display module, wherein the expansion module is used for calling a preset medical article corpus set and acquiring a first type entity subset from the medical article corpus set, and the first type entity subset is used for indicating a plurality of entities with the probability of occurrence larger than a preset threshold value in the same article in the medical article corpus set; calling a preset knowledge graph, and extracting a second entity subset from the knowledge graph, wherein the second entity subset is used for indicating a plurality of disease entities corresponding to the disease conditions in the knowledge graph; determining the first type entity subset and the second type entity subset as entity extension libraries.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the matching module is specifically configured to: acquiring a plurality of candidate doctors in a preset candidate doctor set, and associating the candidate doctors with doctor attribute entities corresponding to each candidate doctor to obtain a doctor attribute entity set of the candidate doctors; calculating entity correlation degree of the doctor attribute entity set and the patient medical entity set to obtain target correlation degree; and comparing a preset target value with the target relevancy, and taking a plurality of corresponding candidate doctors as matching results when the target relevancy is greater than the preset target value.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the generating module is specifically configured to obtain preference data of a user, and perform descending ranking on the plurality of candidate doctors in the candidate doctor set corresponding to the matching result according to the preference data to obtain a target ranking; and taking the candidate doctor with the highest ranking in the target ranking as the doctor matched with the patient.
A third aspect of the present invention provides a doctor-patient matching apparatus, including: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the doctor-patient matching device to perform the doctor-patient matching method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to perform the doctor-patient matching method described above.
According to the technical scheme, symptom data of a patient are obtained, and the symptom data are used for indicating a plurality of corresponding symptom names of the patient; medical entity recognition is carried out on symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the plurality of deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model; acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result; and ranking a plurality of candidate doctors in the candidate doctor set according to the matching result, taking the candidate doctor with the highest ranking as the doctor matched with the patient, and matching the doctor attribute entity set with the patient medical entity set, so that the accuracy of doctor-patient matching is improved.
Drawings
FIG. 1 is a diagram of an embodiment of a doctor-patient matching method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a doctor-patient matching method in an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a doctor-patient matching device in an embodiment of the present invention;
FIG. 4 is a schematic diagram of another embodiment of a doctor-patient matching device in an embodiment of the invention;
fig. 5 is a schematic diagram of an embodiment of a doctor-patient matching device in the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a doctor-patient matching method, a doctor-patient matching device, doctor-patient matching equipment and a storage medium, which are used for improving the accuracy of doctor-patient matching. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a detailed flow of an embodiment of the present invention is described below, and referring to fig. 1, a first embodiment of a doctor-patient matching method in an embodiment of the present invention includes:
101. acquiring symptom data of a patient, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient;
it is understood that the executing subject of the present invention may be a doctor-patient matching device, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
Specifically, in order to ensure the authenticity of the symptom data, the symptom data may be acquired from a medical website, a medical institution database, or the like, the symptom data is text data including a plurality of symptom names, and the symptom data may be acquired from the medical website or the medical institution data through a preset crawler, where the medical website or the medical institution data records the plurality of symptom names of the patient to generate the symptom data, or may directly input the plurality of symptom names by the patient to obtain the symptom data, the server, in the process of acquiring the symptom data of the patient, in order to facilitate distinguishing the symptom data, carries the corresponding symptom tags, and the symptom data of each symptom tag is accompanied by associated data affecting the associated factors of the symptom data.
102. Medical entity recognition is carried out on symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the plurality of deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model;
it should be noted that the patient may enter the symptom data through a medical website. The symptom data can be a disease condition text described by the patient according to the disease condition of the patient. The symptom data generally includes information such as disease symptoms described by the patient, previous medication requests or disease history. Specifically, the patient can input symptom data according to a preset separator, after the symptom data of the patient is obtained, a plurality of illness state symptoms described by the patient can be extracted from the symptom data according to the separator, and the plurality of illness state symptoms jointly form a patient medical entity set matched with the patient. After the server acquires the symptom data of the patient, other symptoms related to the patient's own disease symptom can be inquired through the Internet, and the patient's own disease symptom and the other related symptoms jointly form a patient medical entity set matched with the patient. The server inquires other symptoms related to the patient's own disease symptoms and constructs a patient medical entity set, so that the patient's disease conditions can be more comprehensively described, and the problem that the patient cannot be well hospitalized due to missing of symptom data is avoided.
103. Acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating the candidate doctors to be good at the set corresponding to the treated symptoms;
the candidate doctor may be a doctor who has no visit at the current time on the medical website, and a plurality of candidate doctors together form a candidate doctor set. The candidate doctors can describe the conditions and corresponding symptoms which are respectively good for treatment through written words to obtain the condition descriptions of multiple standards, and the condition descriptions of multiple standards jointly form the doctor attribute entity set. Multiple candidate doctors can also describe multiple conditions and corresponding symptoms which are respectively good for treatment by oral words to obtain multiple popular disease descriptions. The standard disease description and the colloquial disease description together form a set of physician attribute entities.
104. And ranking a plurality of candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
It should be noted that, after the server matches the doctor attribute entity sets of a plurality of candidate doctors with the patient medical entity set, if there are a plurality of candidate doctors matching with the patient, the matched candidate doctors may be ranked according to the seniority or the number of times of visit of the candidate doctors, and the doctor with the highest rank is selected as the doctor matching with the patient; if there is a candidate doctor matching the patient, the candidate doctor is regarded as the doctor matching the patient.
Further, the server stores the matching result in a blockchain database, which is not limited herein.
In the embodiment of the invention, symptom data of a patient is obtained; performing medical entity set matching on the patient according to the symptom data to obtain a patient medical entity set; determining a doctor attribute entity set according to a preset candidate doctor set, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result; and determining the doctor corresponding to the patient according to the matching result and the candidate doctor set, and matching the doctor attribute entity set with the patient medical entity set to improve the accuracy of doctor-patient matching.
Referring to fig. 2, a second embodiment of the doctor-patient matching method according to the embodiment of the present invention includes:
201. acquiring symptom data of a patient, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient;
optionally, the server obtains doctor registration information of a plurality of candidate doctors, and inputs the doctor registration information into a preset entity identification model for entity identification to obtain a registration information entity; the server acquires historical inquiry records of a plurality of candidate doctors, extracts favorable comment records from the historical inquiry records, and performs entity analysis on the favorable comment records to obtain favorable comment record entities; and the server generates a doctor attribute entity set according to the registration information entity and the favorable comment recording entity.
Note that, the doctor registration information may include a plurality of conditions that the doctor is skilled in treating and corresponding symptoms. After the server inputs the doctor registration information into the entity recognition model, the entity recognition model can output a plurality of conditions which are good for treatment of the doctor and corresponding symptoms (namely, the registration information entity). And after the entity identification model outputs the registration information entity, the server stores the registration information entity into the doctor attribute entity set. For example: assuming that the doctor registration information is "chronic cough and bronchitis" in the good field, after the doctor registration information is input into the entity recognition model, the registration information entity output by the entity recognition model is "chronic cough" and "bronchitis". Since the patient in the inquiry records usually communicates with the doctor by spoken words, the communication content in the favorable comment records needs to be physically mined, and the server converts the communication content into a normalized, written expression. The server inputs the good appraisal records into a preset relation entity model, obtains at least one associated good appraisal record output by the relation entity model, then inputs the associated good appraisal record into the entity identification model, and outputs the medical entity corresponding to the good appraisal record through the entity identification model to obtain a good appraisal record entity.
202. Carrying out entity recognition on the symptom data through a preset entity recognition model to obtain a medical entity;
it should be noted that the entity identification model is used to identify entities with specific meaning or strong reference from a sentence, such as time and place from a sentence. After the server inputs the symptom data into the entity recognition model trained in advance, the entity recognition model can output the disease symptoms (namely, medical entities) described by the patient in the symptom data. The entity recognition model may be a Named entity recognition model (NER). The NER model is used for extracting corresponding entities in the text, such as names of people, names of places, quantity, positions and the like, according to predefined entity categories. The NER model may be trained using a plurality of symptom data as training samples, and by inputting the symptom data into the NER model, a plurality of medical entities included in the symptom data may be acquired.
Optionally, the server obtains a target entity subset in a preset entity extension library; the server performs entity matching on the target entity subset and the patient medical entity set to obtain a medical extended entity; the server stores the medical extension entity and the patient medical entity set in an associated mode.
Specifically, before acquiring symptom data of a patient, the server establishes an entity extension library in advance, wherein the entity extension library comprises at least one target entity subset with an association relationship. The server acquires a plurality of groups of disease symptoms with incidence relations from a preset medical website, and constructs an entity expansion library according to the plurality of groups of disease symptoms, wherein each group of disease symptoms in the entity expansion library is a target entity subset with the incidence relations. The server matches the medical entity in need with each target entity subset in the entity expansion library, if one target entity subset has the same entity as the medical entity in need, the server takes the entity as the target entity, and takes other entities except the target entity in the entity subset as medical expansion entities.
For example: when two target entity subsets exist in the entity expansion library, the first target entity subset is toothache, gingival swelling, gingival inflammation and periodontitis, and the second target entity subset is cold, fever, headache, rhinitis and cough, the required medical entities are headache, heatstroke and throat inflammation, and the server matches the required medical entities with the entity expansion library to obtain the medical expansion entities of cold, fever, rhinitis and cough.
203. Carrying out disease identification on symptom data through a preset disease prediction model to obtain a disease entity;
the disease prediction model is used to predict the type of disease in the patient based on the symptom data. After the server inputs the symptom data into the disease prediction model, the disease prediction model may output the type of disease (i.e., disease entity) that the patient may have. The disease prediction model may be trained using a plurality of symptom data as training samples. Before symptom data of a current patient is obtained, a plurality of historical symptom data received by an on-line inquiry platform can be obtained, the plurality of historical symptom data are divided into a training data set and a testing data set, and then iterative training is carried out on a Deep Neural Network (DNN) by using the training data set and the testing data set to obtain a disease prediction model.
Optionally, the server calls a preset medical article corpus set, and obtains a first-class entity subset from the medical article corpus set, where the first-class entity subset is used to indicate multiple entities with probability greater than a preset threshold appearing in the same article in the medical article corpus set; the server calls a preset knowledge graph and extracts a second entity subset from the knowledge graph, wherein the second entity subset is used for indicating a plurality of disease entities corresponding to the disease conditions in the knowledge graph; the server determines the first type entity subset and the second type entity subset as entity extension libraries.
It should be noted that the corpus of medical articles includes all medical articles published by doctors in all departments, and a large amount of historical query records collected in medical websites. After the server obtains the corpus set of the medical articles, determining a plurality of entities with the common occurrence probability larger than a preset threshold value in the same article or the same inquiry record as entities with a co-occurrence relationship, and then forming the entities with the co-occurrence relationship into a first type entity subset. In addition, the knowledge profile can be a pre-established, structured graph reflecting the relationship between a number of different conditions and associated disorders and administration forms. In the knowledge map, the relationship between each disease condition and related disease and medication forms is called map relationship. The server obtains the related symptoms of each condition according to the knowledge map, and forms each condition and the related symptoms into a second type entity subset.
204. Performing associated entity identification on the symptom data through a preset relationship entity model to obtain an associated medical entity;
it should be noted that the relational entity model may be an Approximate Nearest Neighbor search model (ANN). The ANN model is used for obtaining a plurality of word vectors corresponding to symptom data, searching nearest neighbor word vectors in a vector space according to the word vectors, the ANN model can be obtained by training by using the symptom data as training samples, and relevant medical entities corresponding to the symptom data can be obtained by inputting the symptom data into the ANN model.
Specifically, after the server acquires the symptom data of the patient, other symptoms related to the patient's own disease symptoms can be inquired through the internet, and the patient's own disease symptoms and the other related symptoms together form a patient medical entity set matched with the patient.
205. Determining a medical entity, a disease entity, and an associated medical entity as a set of patient medical entities;
specifically, the server inquires other symptoms related to the patient's own disease symptoms and constructs a patient medical entity set, so that the disease condition of the patient can be described more comprehensively, and the problem that the patient cannot be well hospitalized due to missing of symptom data is avoided.
206. Acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating the candidate doctors to be good at the set corresponding to the treated symptoms;
optionally, the server obtains a plurality of candidate doctors in a preset candidate doctor set, and associates the plurality of candidate doctors with the doctor attribute entity corresponding to each candidate doctor to obtain a doctor attribute entity set of the plurality of candidate doctors; the server calculates the entity relevance between the doctor attribute entity set and the patient medical entity set to obtain the target relevance; and the server compares the preset target value with the target relevancy, and takes a plurality of corresponding candidate doctors as matching results when the target relevancy is greater than the preset target value.
Specifically, after the server acquires the patient medical entity set matched with the patient and the doctor attribute entity sets of the plurality of candidate doctors, the condition symptoms included in the patient medical entity set may be compared with the condition symptoms included in each doctor attribute entity set to find the candidate doctors matched with the patient. The server matches each doctor attribute entity set with the patient medical entity set, so that doctors with higher relevance can be matched for the patient, and the inquiry experience of the patient is improved; secondly, some doctors with specific specialties can be matched with related patients, and further waste of medical resources can be avoided.
207. And ranking a plurality of candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
Optionally, the server obtains preference data of the user, and performs descending ranking on a plurality of candidate doctors in the candidate doctor set corresponding to the matching result according to the preference data to obtain target ranking; taking the candidate doctor with the highest rank in the target ranking as the doctor matched with the patient
Specifically, the server ranks the matched candidate doctors according to seniority or number of times of treatment of the candidate doctors, and obtains target ranking, and selects the doctor with the highest rank as the doctor matched with the patient.
Further, the server stores the matching result in a blockchain database, which is not limited herein.
In the embodiment of the invention, symptom data of a patient is obtained; performing medical entity set matching on the patient according to the symptom data to obtain a patient medical entity set; determining a doctor attribute entity set according to a preset candidate doctor set, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result; and determining the doctor corresponding to the patient according to the matching result and the candidate doctor set, and matching the doctor attribute entity set with the patient medical entity set to improve the accuracy of doctor-patient matching.
In the above description of the doctor-patient matching method in the embodiment of the present invention, referring to fig. 3, a doctor-patient matching device in the embodiment of the present invention is described below, and a first embodiment of the doctor-patient matching device in the embodiment of the present invention includes:
an obtaining module 301, configured to obtain symptom data of a patient, where the symptom data is used to indicate a plurality of symptom names corresponding to the patient;
the processing module 302 is configured to perform medical entity identification on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, where the plurality of deep learning models include an entity identification model, a disease prediction model, and a relationship entity model;
a matching module 303, configured to obtain a plurality of candidate doctors in a preset candidate doctor set, determine a doctor attribute entity set corresponding to the plurality of candidate doctors, and perform entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, where the doctor attribute entity set is used to indicate that the plurality of candidate doctors are good at a set corresponding to a treated disease condition;
a generating module 304, configured to rank the multiple candidate doctors in the candidate doctor set according to the matching result, and use the candidate doctor with the highest rank as the doctor matched with the patient.
In the embodiment of the invention, symptom data of a patient is obtained, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient; medical entity recognition is carried out on symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the plurality of deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model; acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result; and ranking the candidate doctors in the candidate doctor set according to the matching result, taking the candidate doctor with the highest ranking as the doctor matched with the patient, and matching the doctor attribute entity set with the patient medical entity set, so that the accuracy of doctor-patient matching is improved.
Referring to fig. 4, a second embodiment of the doctor-patient matching device in the embodiment of the present invention includes:
an obtaining module 301, configured to obtain symptom data of a patient, where the symptom data is used to indicate a plurality of symptom names corresponding to the patient;
the processing module 302 is configured to perform medical entity identification on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, where the plurality of deep learning models include an entity identification model, a disease prediction model, and a relationship entity model;
a matching module 303, configured to obtain a plurality of candidate doctors in a preset candidate doctor set, determine a doctor attribute entity set corresponding to the plurality of candidate doctors, and perform entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, where the doctor attribute entity set is used to indicate that the plurality of candidate doctors are good at a set corresponding to a treated disease condition;
a generating module 304, configured to rank the multiple candidate doctors in the candidate doctor set according to the matching result, and use the candidate doctor with the highest rank as the doctor matched with the patient.
Optionally, the doctor-patient matching device further comprises:
a configuration module 305, configured to obtain doctor registration information of the multiple candidate doctors, and input the doctor registration information into a preset entity identification model for entity identification, so as to obtain a registration information entity; obtaining historical inquiry records of the candidate doctors, extracting favorable evaluation records from the historical inquiry records, and performing entity analysis on the favorable evaluation records to obtain favorable evaluation record entities; and generating the doctor attribute entity set according to the registration information entity and the favorable comment recording entity.
Optionally, the processing module 302 is specifically configured to:
carrying out entity recognition on the symptom data through a preset entity recognition model to obtain a medical entity; carrying out disease identification on the symptom data through a preset disease prediction model to obtain a disease entity; performing associated entity identification on the symptom data through a preset relationship entity model to obtain an associated medical entity; determining the medical entity, the disease entity, and the associated medical entity as the set of patient medical entities.
Optionally, the doctor-patient matching device further comprises:
a storage module 306, configured to obtain a target entity subset in a preset entity extension library; performing entity matching on the target entity subset and the patient medical entity set to obtain a medical extended entity; and performing associated storage on the medical extension entity and the patient medical entity set.
Optionally, the doctor-patient matching device further comprises:
an extension module 307, configured to call a preset medical article corpus set, and obtain a first-class entity subset from the medical article corpus set, where the first-class entity subset is used to indicate multiple entities with occurrence probabilities greater than a preset threshold in a same article in the medical article corpus set; calling a preset knowledge graph, and extracting a second entity subset from the knowledge graph, wherein the second entity subset is used for indicating a plurality of disease entities corresponding to the disease conditions in the knowledge graph; determining the first type entity subset and the second type entity subset as entity extension libraries.
Optionally, the matching module 303 is specifically configured to:
acquiring a plurality of candidate doctors in a preset candidate doctor set, and associating the candidate doctors with doctor attribute entities corresponding to each candidate doctor to obtain a doctor attribute entity set of the candidate doctors; calculating entity correlation degree of the doctor attribute entity set and the patient medical entity set to obtain target correlation degree; and comparing a preset target value with the target relevancy, and taking a plurality of corresponding candidate doctors as matching results when the target relevancy is greater than the preset target value.
Optionally, the generating module 304 is specifically configured to:
acquiring preference data of a user, and performing descending ranking on the candidate doctors in the candidate doctor set corresponding to the matching result according to the preference data to obtain target ranking; and taking the candidate doctor with the highest ranking in the target ranking as the doctor matched with the patient.
In the embodiment of the invention, symptom data of a patient is obtained, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient; determining a set of patient medical entities matching the symptom data; acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and a patient medical entity set to obtain a matching result; and ranking a plurality of candidate doctors in the candidate doctor set according to the matching result, taking the candidate doctor with the highest ranking as the doctor matched with the patient, and matching the doctor attribute entity set with the patient medical entity set, so that the accuracy of doctor-patient matching is improved.
Fig. 3 and 4 describe the doctor-patient matching device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the doctor-patient matching device in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of a doctor-patient matching apparatus according to an embodiment of the present invention, where the doctor-patient matching apparatus 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instructions for operating on the doctor-patient matching apparatus 500. Still further, the processor 510 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the doctor-patient matching device 500.
The doctor-patient matching device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that the patient and patient matching device configuration shown in fig. 5 does not constitute a limitation of the patient and patient matching device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
The invention further provides a doctor-patient matching device, which comprises a memory and a processor, wherein computer readable instructions are stored in the memory, and when the computer readable instructions are executed by the processor, the processor executes the steps of the doctor-patient matching method in the above embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the doctor-patient matching method.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A doctor-patient matching method is characterized by comprising the following steps:
acquiring symptom data of a patient, wherein the symptom data is used for indicating a plurality of corresponding symptom names of the patient;
medical entity recognition is carried out on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, wherein the deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model;
acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating a set corresponding to symptoms which are treated by the candidate doctors well;
and ranking the candidate doctors in the candidate doctor set according to the matching result, and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
2. The doctor-patient matching method according to claim 1, wherein before the obtaining symptom data of the patient, the symptom data indicating a plurality of symptom names corresponding to the patient, the doctor-patient matching method further comprises:
acquiring doctor registration information of the candidate doctors, and inputting the doctor registration information into a preset entity identification model for entity identification to obtain a registration information entity;
obtaining historical inquiry records of the candidate doctors, extracting favorable evaluation records from the historical inquiry records, and performing entity analysis on the favorable evaluation records to obtain favorable evaluation record entities;
and generating the doctor attribute entity set according to the registration information entity and the favorable comment recording entity.
3. The doctor-patient matching method according to claim 1, wherein the medical entity recognition is performed on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the plurality of deep learning models include an entity recognition model, a disease prediction model and a relationship entity model, and include:
carrying out entity recognition on the symptom data through a preset entity recognition model to obtain a medical entity;
carrying out disease identification on the symptom data through a preset disease prediction model to obtain a disease entity;
performing associated entity identification on the symptom data through a preset relationship entity model to obtain an associated medical entity;
determining the medical entity, the disease entity, and the associated medical entity as the set of patient medical entities.
4. The doctor-patient matching method according to claim 1, wherein after the obtaining of symptom data of a patient, the symptom data indicating a plurality of symptom names corresponding to the patient, the medical entity recognition is performed on the symptom data through a plurality of deep learning models which are preset to obtain a patient medical entity set, and before the plurality of deep learning models include an entity recognition model, a disease prediction model and a relationship entity model, the doctor-patient matching method further includes:
acquiring a target entity subset in a preset entity expansion library;
performing entity matching on the target entity subset and the patient medical entity set to obtain a medical extended entity;
and performing associated storage on the medical extension entity and the patient medical entity set.
5. The doctor-patient matching method according to claim 4, wherein before the obtaining of the target entity subset in the preset entity expansion library, the doctor-patient matching method further comprises:
calling a preset medical article corpus set, and acquiring a first-class entity subset from the medical article corpus set, wherein the first-class entity subset is used for indicating a plurality of entities with the probability of occurrence being larger than a preset threshold value in the same article in the medical article corpus set;
calling a preset knowledge graph, and extracting a second entity subset from the knowledge graph, wherein the second entity subset is used for indicating a plurality of disease entities corresponding to the disease conditions in the knowledge graph;
determining the first type entity subset and the second type entity subset as entity extension libraries.
6. The doctor-patient matching method according to claim 1, wherein the obtaining of a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the plurality of candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result includes:
acquiring a plurality of candidate doctors in a preset candidate doctor set, and associating the candidate doctors with doctor attribute entities corresponding to each candidate doctor to obtain a doctor attribute entity set of the candidate doctors;
calculating entity correlation degree of the doctor attribute entity set and the patient medical entity set to obtain target correlation degree;
and comparing a preset target value with the target relevancy, and taking a plurality of corresponding candidate doctors as matching results when the target relevancy is greater than the preset target value.
7. The doctor-patient matching method according to any one of claims 1 to 6, wherein the ranking the plurality of candidate doctors in the candidate doctor set according to the matching result and using the highest ranked candidate doctor as the doctor matched with the patient comprises:
acquiring preference data of a user, and performing descending ranking on the candidate doctors in the candidate doctor set corresponding to the matching result according to the preference data to obtain target ranking;
and taking the candidate doctor with the highest ranking in the target ranking as the doctor matched with the patient.
8. A doctor-patient matching device, characterized in that it comprises:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring symptom data of a patient, and the symptom data is used for indicating a plurality of symptom names corresponding to the patient;
the processing module is used for carrying out medical entity recognition on the symptom data through a plurality of preset deep learning models to obtain a patient medical entity set, and the deep learning models comprise an entity recognition model, a disease prediction model and a relation entity model;
the matching module is used for acquiring a plurality of candidate doctors in a preset candidate doctor set, determining a doctor attribute entity set corresponding to the candidate doctors, and performing entity matching on the doctor attribute entity set and the patient medical entity set to obtain a matching result, wherein the doctor attribute entity set is used for indicating a set corresponding to symptoms which are treated by the candidate doctors;
and the generating module is used for ranking the candidate doctors in the candidate doctor set according to the matching result and taking the candidate doctor with the highest ranking as the doctor matched with the patient.
9. A doctor-patient matching device, characterized in that it comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the doctor-patient matching device to perform the doctor-patient matching method of any one of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement the doctor-patient matching method according to any one of claims 1-7.
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