CN114743647A - Medical data processing method, device, equipment and storage medium - Google Patents

Abstract

The invention relates to the field of artificial intelligence, and discloses a medical data processing method, device, equipment and storage medium, which are used for improving the accuracy of doctor-patient matching. The medical data processing method includes: judging whether the user conforms to the user type according to the medical data; if the user conforms to the user type, matching a disease template corresponding to the user according to the medical data; filling the disease template with information based on the disease diagnosis path to obtain an end node and user answer path, and generate user demand according to the end node and user answer path; if the user demand is a doctor's consultation, the medical data and user answer path are input into the medical data processing model for disease data processing, and the prediction node is obtained; Compare and analyze with the end node. If the comparison result is the same, the user will be matched with a doctor to obtain the target doctor. In addition, the present invention also relates to blockchain technology, and user requirements can be stored in blockchain nodes.

Description

Medical data processing method, device, equipment and storage medium

technical field

The present invention relates to the field of artificial intelligence, and in particular, to a medical data processing method, device, equipment and storage medium.

Background technique

With the development of medical Internet and informatization, services such as online appointment registration, remote doctor consultation, prescription recommendation, opening and drug purchase have provided convenience to patients and helped to digest the heavy business of some offline hospitals.

The common practice of online consultation is to first match departments and doctors based on patient complaints and basic information, and usually use some classification models to classify patients. At present, the satisfaction of matching patients and doctors needs to be improved, mainly because of the lack of user information. It is difficult to match the data of hospital doctors with a large amount, many sources and different data structures, that is, the accuracy of the existing scheme is low.

SUMMARY OF THE INVENTION

The present invention provides a medical data processing method, device, equipment and storage medium, which are used to improve the accuracy of doctor-patient matching.

A first aspect of the present invention provides a medical data processing method, the medical data processing method includes: acquiring medical data corresponding to a user to be processed, and judging whether the user conforms to a preset user type according to the medical data, The medical data includes user gender, user age and symptom data; if the user conforms to a preset user type, a disease template corresponding to the user is matched according to the medical data, wherein the disease template includes The disease diagnosis path to be filled in by the user; the disease template is filled with information based on the disease diagnosis path, to obtain the end node and user answer path corresponding to the disease template, and generate according to the end node and the user answer path User requirement; if the user requirement is a doctor's consultation, input the medical data and the user answer path into a preset medical data processing model for disease data processing, and obtain a prediction node; The end node is compared and analyzed, and a comparison result is obtained; if the comparison result is that the prediction node and the end node are the same, the user is matched with a doctor according to the prediction node and the end node, and the result is obtained target doctor.

Optionally, in the first implementation manner of the first aspect of the present invention, the obtaining medical data corresponding to the user to be processed, and judging whether the user conforms to a preset user type according to the medical data, includes: receiving medical data input by a user to be processed, wherein the medical data includes user gender, user age and symptom data; generating a user portrait corresponding to the user based on the user gender, the user age and the symptom data; Calculate whether the user portrait conforms to the similarity corresponding to the preset user type, and compare the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, determine the The user described above matches the preset user type.

Optionally, in the second implementation manner of the first aspect of the present invention, if the user conforms to a preset user type, matching a disease template corresponding to the user according to the medical data, including: If the user conforms to a preset user type, keyword extraction is performed on the medical data to obtain a target keyword; the target keyword is respectively matched with a plurality of preset disease templates to obtain a corresponding disease template corresponding to each disease template. The keyword hit rate corresponding to each disease template is sorted, and the disease template with the highest keyword hit rate is used as the disease template corresponding to the user, wherein the disease template includes the user's disease template. Fill in the disease diagnosis path.

Optionally, in a third implementation manner of the first aspect of the present invention, the disease template is filled with information based on the disease diagnosis path to obtain an end node and a user answer path corresponding to the disease template, and Generating user requirements according to the end node and the user answer path includes: obtaining a pass-through rate and a recommended click-through rate based on the disease diagnosis path; Filling the information to obtain the filled disease template; extracting data from the filled disease template to obtain the end node and user answer path corresponding to the disease template; generating user requirements based on the end node and the user answer path , wherein the user requirements include doctor consultation and offline consultation.

Optionally, in the fourth implementation manner of the first aspect of the present invention, if the user demand is a doctor's consultation, the medical data and the user's answer path are input into a preset medical data processing model. Perform disease data processing to obtain a prediction node, including: if the user demand is a doctor's consultation, inputting the medical data and the user answer path into a preset medical data processing model; The convolutional neural network of the device performs a convolution operation on the medical data and the user's answer path to obtain a feature vector corresponding to the medical data; and performs feature normalization on the feature vector to obtain a prediction node.

Optionally, in a fifth implementation manner of the first aspect of the present invention, if the comparison result is that the prediction node and the end node are the same, then according to the prediction node and the end node pair The user performs doctor matching to obtain the target doctor, including: if the comparison result is that the prediction node and the end node are the same, acquiring multiple candidate doctors in the preset candidate doctor set; based on the prediction node Perform credibility calculation on the multiple candidate doctors with the end node to obtain the target credibility corresponding to each candidate doctor; according to the target credibility corresponding to each candidate doctor The multiple candidate doctors are ranked, and the highest ranked candidate doctor is used as the doctor matching the user to obtain the target doctor.

Optionally, in the sixth implementation manner of the first aspect of the present invention, if the comparison result is that the prediction node and the end node are the same, according to the prediction node and the end node After performing doctor matching on the user and obtaining the target doctor, the medical data processing method further includes: if the comparison result is that the prediction node and the end node are different, deleting the prediction node, and generate auxiliary information corresponding to the user based on the disease template; perform doctor matching on the user based on the auxiliary information and the end node to obtain a target doctor corresponding to the user.

A second aspect of the present invention provides a medical data processing apparatus, the medical data processing apparatus includes: an acquisition module, configured to acquire medical data corresponding to a user to be processed, and determine whether the user meets the predetermined requirements according to the medical data. A preset user type, wherein the medical data includes user gender, user age and symptom data; a matching module is configured to match the user corresponding to the user according to the medical data if the user conforms to the preset user type. A disease template, wherein the disease template includes a disease diagnosis path to be filled in by a user; a filling module is used to fill the disease template with information based on the disease diagnosis path, and obtain the end node corresponding to the disease template and the user answer path, and generate user requirements according to the end node and the user answer path; the processing module is used to input the medical data and the user answer path into the preset medical treatment if the user requirement is a doctor's consultation The data processing model performs disease data processing to obtain a prediction node; an analysis module is used to compare and analyze the prediction node and the end node to obtain a comparison result; a generation module is used if the comparison result is all If the prediction node and the end node are the same, the user is matched with a doctor according to the prediction node and the end node to obtain a target doctor.

Optionally, in the first implementation manner of the second aspect of the present invention, the acquisition module is specifically configured to: receive medical data input by the user to be processed, wherein the medical data includes user gender, user age and symptoms data; generate a user portrait corresponding to the user based on the user gender, the user age and the symptom data; calculate whether the user portrait conforms to the preset similarity corresponding to the user type, and compare the similarity and The preset target value is compared; if the similarity is greater than or equal to the preset target value, it is determined that the user conforms to a preset user type.

Optionally, in the second implementation manner of the second aspect of the present invention, the matching module is specifically configured to: if the user conforms to a preset user type, perform keyword extraction on the medical data to obtain the target The target keyword is matched with a plurality of preset disease templates, respectively, to obtain the keyword hit rate corresponding to each disease template; the keyword hit rate corresponding to each disease template is sorted, and all The disease template with the highest keyword hit rate is used as the disease template corresponding to the user, wherein the disease template includes the disease diagnosis path to be filled in by the user.

Optionally, in a third implementation manner of the second aspect of the present invention, the filling module is specifically configured to: obtain a pass-through rate and a recommended click-through rate based on the disease diagnosis path; The recommended click rate fills the disease template with information to obtain a filled disease template; performs data extraction on the filled disease template to obtain an end node and a user answer path corresponding to the disease template; based on the end node and the user answer path to generate user requirements, wherein the user requirements include doctor consultation and offline consultation.

Optionally, in a fourth implementation manner of the second aspect of the present invention, the processing module is specifically configured to: if the user demand is a doctor's consultation, input the medical data and the user's answer path into a preset. The medical data processing model set in the medical data processing model; the convolution operation is performed on the medical data and the user answer path through the convolutional neural network in the medical data processing model to obtain the feature vector corresponding to the medical data; The vector is subjected to feature normalization processing to obtain the prediction node.

Optionally, in a fifth implementation manner of the second aspect of the present invention, the generation module is specifically configured to: if the comparison result is that the prediction node and the end node are the same, obtain a preset candidate doctor. multiple candidate doctors in the set; perform credibility calculation on the multiple candidate doctors based on the prediction node and the end node, and obtain the target credibility corresponding to each candidate doctor; The target reliability ranks the multiple candidate doctors in the candidate doctor set, and uses the highest ranked candidate doctor as the doctor matched with the user to obtain the target doctor.

Optionally, in a sixth implementation manner of the second aspect of the present invention, the medical data processing apparatus further includes: a deletion module, configured to, if the comparison result is that the prediction node and the end node are different, , the prediction node is deleted, and auxiliary information corresponding to the user is generated based on the disease template; based on the auxiliary information and the end node, the user is matched with a doctor, and the target corresponding to the user is obtained. doctor.

A third aspect of the present invention provides a medical data processing device, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory, so that all The medical data processing apparatus executes the medical data processing method described above.

A fourth aspect of the present invention provides a computer-readable storage medium storing instructions in the computer-readable storage medium, which, when executed on a computer, cause the computer to execute the above-mentioned medical data processing method.

In the technical solution provided by the present invention, whether the user conforms to the preset user type is determined according to medical data, wherein the medical data includes user gender, user age and symptom data; if the user conforms to the preset user type, the medical data is matched with The disease template corresponding to the user, wherein the disease template includes the disease diagnosis path to be filled in by the user; fill the disease template with information based on the disease diagnosis path to obtain the end node corresponding to the disease template and the user answer path, and according to the end node and the user answer path Generate user demand; if the user demand is a doctor's consultation, input the medical data and the user answer path into the preset medical data processing model for disease data processing, and obtain the prediction node; If the comparison result is that the prediction node and the end node are the same, the user is matched with a doctor according to the prediction node and the end node, and the target doctor is obtained. This part of the more comprehensive information can be used for more accurate doctor-patient matching, which avoids some invalid communication or wrong matching, and conducts user self-diagnosis and medical needs identification according to the disease diagnosis path template, which improves the efficiency of the online consultation process.

Description of drawings

FIG. 1 is a schematic diagram of an embodiment of a medical data processing method in an embodiment of the present invention;

2 is a schematic diagram of another embodiment of a medical data processing method in an embodiment of the present invention;

3 is a schematic diagram of an embodiment of a medical data processing apparatus in an embodiment of the present invention;

4 is a schematic diagram of another embodiment of a medical data processing apparatus in an embodiment of the present invention;

FIG. 5 is a schematic diagram of an embodiment of a medical data processing device in an embodiment of the present invention.

Detailed ways

Embodiments of the present invention provide a medical data processing method, apparatus, device, and storage medium, which are used to improve the accuracy of doctor-patient matching. The terms "first", "second", "third", "fourth", etc. (if present) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that data so used may be interchanged under appropriate circumstances so that the embodiments described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those expressly listed steps or units, but may include other steps or units not expressly listed or inherent to these processes, methods, products or devices.

For ease of understanding, the specific flow of the embodiment of the present invention is described below, referring to Fig. 1, the first embodiment of the medical data processing method in the embodiment of the present invention includes:

101. Obtain the medical data corresponding to the user to be processed, and determine whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data;

It should be noted that the medical data input by the user to be processed includes personal gender, age and symptom data. User types include not knowing their own diseases, unclear needs for consultation, or actively choosing intelligent disease diagnosis. The server identifies the user type according to the medical data input by the user, and determines whether the user to be processed conforms to the above user type. It should be noted that, in order to ensure the authenticity of the symptom data, the symptom data can be obtained from medical websites, medical institution databases, etc. Symptom data is text data including disease vocabulary and symptom vocabulary. Symptom data can be obtained from medical website or medical institution data through a preset crawler, wherein the medical website or medical institution data records the user's disease vocabulary and symptom vocabulary to generate symptom data. , or the user can directly input disease vocabulary and symptom vocabulary to obtain symptom data.

It can be understood that the execution subject of the present invention may be a medical data processing device, and may also be a terminal or a server, which is not specifically limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example. The embodiments of the present invention can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning. The server can be an independent server, or can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN) ), as well as cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.

102. If the user conforms to the preset user type, the disease template corresponding to the user is matched according to the medical data, wherein the disease template includes the disease diagnosis path to be filled in by the user;

Specifically, if the user meets the preset user type, the server enters the self-diagnosis module. The server first matches the disease template according to the previous information. The disease template contains the disease diagnosis path, similar to a tree diagram, including multiple question options and different answers. May lead to different branches. By the user answering the questions in the template, the system will populate the template nodes and throw new questions until the diagnostic node or end node is reached. It should be noted that the disease template is obtained through computer-assisted medical literature mining plus doctor editing and review.

103. Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements according to the end node and the user answer path;

It should be noted that when the user fills in the problems in the template, the server will fill the template nodes and throw new problems until all the diagnosis nodes or end nodes are filled. According to the final node and the user's answer path in the middle, the server predicts a user's needs through the deep learning network model, including doctor consultation, hospital search, medicine purchase, physical examination, registration, chronic disease management, comprehensive consultation, etc. Recommend related functions and guide users to the next service module.

104. If the user's demand is a doctor's consultation, the medical data and the user's answer path are input into the preset medical data processing model for disease data processing, and a prediction node is obtained;

Specifically, if the user's request is a doctor's consultation, the server inputs the medical data and the user's answer path into a preset medical data processing model for disease data processing, wherein the preset medical data processing model includes an input layer, a multi-layer convolution Neural network and normalization layer, among which, the multi-layer convolutional neural network is used to perform convolution operation on the input vector, and the normalization layer is used to perform logistic regression operation on the feature vector, and finally output the prediction node.

105. Compare and analyze the prediction node and the end node to obtain a comparison result;

Specifically, the server compares and analyzes the prediction node and the end node, and obtains a comparison result, wherein the server calculates the similarity between the prediction node and the end node, and when the similarity exceeds a preset preset, the server determines the prediction node. Like the end node, the server determines that the prediction node is different from the end node when the similarity does not exceed the preset threshold.

106. If the comparison result is that the prediction node and the end node are the same, perform doctor matching on the user according to the prediction node and the end node to obtain the target doctor.

Specifically, if the comparison result is that the prediction node and the end node are the same, the server calculates the reliability of the prediction node and the end node to obtain the target reliability. The server sorts the target reliability and selects the target reliability. The degree corresponds to the maximum value of the candidate doctor, the maximum value of the candidate doctor is obtained, and the server takes the candidate doctor corresponding to the maximum value as the target doctor.

Further, the server stores the user requirements in the blockchain database, which is not specifically limited here.

In this embodiment of the present invention, it is determined whether the user conforms to a preset user type according to medical data, wherein the medical data includes user gender, user age and symptom data; if the user conforms to the preset user type, the medical data is matched with the user corresponding to the user disease template, wherein the disease template includes the disease diagnosis path to be filled in by the user; fill the disease template with information based on the disease diagnosis path, obtain the end node corresponding to the disease template and the user answer path, and generate the user according to the end node and the user answer path. Demand; if the user's demand is a doctor's consultation, the medical data and the user's answer path are input into the preset medical data processing model for disease data processing, and the prediction node is obtained; the prediction node and the end node are compared and analyzed to obtain the comparison result. If the comparison result is that the prediction node and the end node are the same, then the user is matched with a doctor according to the prediction node and the end node, and the target doctor is obtained. This part of the more comprehensive information is used for more accurate doctor-patient matching, which avoids some invalid communication or wrong matching. User self-diagnosis and medical needs identification are carried out according to the disease diagnosis path template, which improves the efficiency of the online consultation process.

Referring to FIG. 2, the second embodiment of the medical data processing method in the embodiment of the present invention includes:

201, obtain the medical data corresponding to the user to be processed, and judge whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data;

Specifically, the server receives medical data input by the user to be processed, wherein the medical data includes user gender, user age and symptom data; the server generates a user portrait corresponding to the user based on the user gender, user age and symptom data; the server calculates whether the user portrait is The similarity corresponding to the preset user type is met, and the similarity is compared with the preset target value; if the similarity is greater than or equal to the preset target value, the server determines that the user meets the preset user type. Specifically, if the similarity is less than the preset target value, the server determines that the user does not meet the preset user type, the server receives the medical data input by the user to be processed, the user can input the medical data through the preset terminal, and the medical data includes The user's personal information and symptom data; the server generates a user portrait corresponding to the user based on the user's gender, user age and symptom data, and the server generates a triplet corresponding to the user, which includes gender, age and symptoms; the server calculates whether the user portrait is It matches the similarity corresponding to the preset user type, and compares the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, the server determines that the user meets the preset user type. If the preset target value is set, the server determines that the user does not conform to the above three types of users.

202. If the user conforms to the preset user type, the disease template corresponding to the user is matched according to the medical data, wherein the disease template includes the disease diagnosis path to be filled in by the user;

Specifically, if the user conforms to the preset user type, the server performs keyword extraction on the medical data to obtain the target keyword; the server matches the target keyword with a plurality of preset disease templates respectively, and obtains the corresponding disease template corresponding to each disease template. The server sorts the keyword hit rate corresponding to each disease template, and uses the disease template with the highest keyword hit rate as the disease template corresponding to the user, wherein the disease template includes the disease diagnosis path to be filled in by the user . Specifically, if the user conforms to the preset user type, the server performs keyword extraction on the medical data to obtain the target keyword. The server performs keyword extraction by first converting the medical data into text data, and then using the preset OCR character recognition. The model extracts the text data to obtain standard text data, and finally removes the duplicate content of the standard text data to obtain the target keyword; the server matches the target keyword with multiple preset disease templates respectively, and obtains each disease template The corresponding keyword hit rate, the keyword hit rate is the number of keyword hits; the server sorts the keyword hit rate corresponding to each disease template, and uses the disease template with the highest keyword hit rate as the disease corresponding to the user A template, wherein the disease template includes a disease diagnosis path to be filled in by the user.

203. Fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate user requirements according to the end node and the user answer path;

Specifically, the server obtains the passing rate and the recommended click rate based on the disease diagnosis path; the server fills the disease template with information based on the passing rate and the recommended click rate to obtain the filled disease template; the server extracts data from the filled disease template to obtain the end node and user answer path corresponding to the disease template; the server generates user requirements based on the end node and the user answer path, wherein the user requirements include doctor consultation and offline consultation. Specifically, the server obtains the pass-through rate and the recommended click-through rate based on the disease diagnosis path. The server optimizes the template through indicators such as the pass-through rate and the recommended click-through rate obtained through statistical analysis of online business data. The pass-through rate refers to whether the user can walk or not. To the final node of the template, the server fills the disease template with information based on the running rate and the recommended click rate, and obtains the filled disease template; the server extracts data from the filled disease template to obtain the corresponding end node of the disease template and the user's answer. Path; the server generates user requirements based on the end node and the user answer path, wherein the user requirements include doctor consultation and offline consultation. Among them, a template can be used as a node of other templates, that is, a sub-template. There may be an inclusion relationship between templates. For example, a general ophthalmology template may contain several ophthalmic disease templates as sub-templates, and the weight of template nodes will be adjusted according to the user's historical medical records such as consultation information.

204. If the user's demand is a doctor's consultation, input the medical data and the user's answer path into a preset medical data processing model to process the disease data, and obtain a prediction node;

Specifically, if the user's request is for a doctor's consultation, the server will input the medical data and the user's answer path into the preset medical data processing model; the server will roll the medical data and the user's answer path through the convolutional neural network in the medical data processing model. The product operation is performed to obtain the feature vector corresponding to the medical data; the server performs feature normalization processing on the feature vector to obtain the prediction node. It should be noted that the preset medical data processing model includes an input layer, a convolutional neural network and an output layer, the input layer: a one-hot vector encoding layer; the hidden layer: a convolution operation function, that is, a linear unit; output layer: the dimension is the same as that of the input layer, using logistic regression. The server performs one-hot vector encoding on the target filling information through the input layer to obtain a low-dimensional vector. For example, the low-dimensional vector is [0,0,0,1,0,1,0,0]. The low-dimensional vector performs feature abstraction operations to obtain abstract feature values, which are also feature vectors; the server performs logistic regression operations on the abstract feature values through the output layer to obtain prediction nodes, where the logistic regression operation is a softmax regression operation, where, The prediction node can be the result of the initial online diagnosis, for example: the initial diagnosis of blepharitis, conjunctivitis, respiratory tract infection, etc. online.

205. Compare and analyze the prediction node and the end node to obtain a comparison result;

Specifically, the server compares and analyzes the prediction node and the end node, and obtains a comparison result, wherein the server calculates the similarity between the prediction node and the end node, and when the similarity exceeds a preset preset, the server determines the prediction node. Like the end node, the server determines that the prediction node is different from the end node when the similarity does not exceed the preset threshold.

206. If the comparison result is that the prediction node and the end node are the same, then perform doctor matching on the user according to the prediction node and the end node to obtain the target doctor;

Specifically, if the comparison result is that the prediction node and the end node are the same, the server obtains multiple candidate doctors in the preset candidate doctor set; the server calculates the reliability of the multiple candidate doctors based on the prediction node and the end node, and obtains each candidate doctor. target reliability corresponding to each candidate doctor; the server ranks multiple candidate doctors in the candidate doctor set according to the target reliability corresponding to each candidate doctor, and takes the highest ranked candidate doctor as the doctor matching the user, and obtains: target doctor. Specifically, the server sorts the preset candidate doctor list based on the target reliability to obtain the ranking. The server is based on the user and considers the user's preference and the workload of the candidate doctor. The server defines the user's preference degree to measure the user's preference for the candidate doctor. Preference index, the server obtains the preference index corresponding to the user through the weighted average method of the doctor-patient matching index. After the server calculates the matching degree of the doctors in all the candidate doctors to the user, it recommends the target doctor according to the highest target reliability and preference index. The server matches users with highly relevant target doctors to improve the user's consultation experience. In addition, some target doctors with specific expertise can also be matched with relevant users, thereby avoiding the waste of medical resources.

207. If the comparison result is that the prediction node and the end node are different, delete the prediction node, and generate auxiliary information corresponding to the user based on the disease template;

Specifically, if the comparison result is that the prediction node and the end node are the same, the server calculates the reliability of the prediction node and the end node to obtain the target reliability. The server sorts the target reliability and selects the target reliability. The degree corresponds to the maximum value of the candidate doctor, the maximum value of the candidate doctor is obtained, and the server takes the candidate doctor corresponding to the maximum value as the target doctor.

208. Perform doctor matching on the user based on the auxiliary information and the end node to obtain a target doctor corresponding to the user.

Specifically, if the result of the server does not match, the matching doctor model will remove the diagnosis result information, and use the information of the department, the chief complaint and some nodes to match the doctor. Template questions answered by the user are also transmitted to the doctor as auxiliary information. Based on the auxiliary information and the end node, the server performs doctor matching on the user, and obtains the target doctor corresponding to the user.

Further, the server stores the user requirements in the blockchain database, which is not specifically limited here.

In this embodiment of the present invention, it is determined whether the user conforms to a preset user type according to medical data, wherein the medical data includes user gender, user age and symptom data; if the user conforms to the preset user type, the medical data is matched with the user corresponding to the user disease template, wherein the disease template includes the disease diagnosis path to be filled in by the user; fill the disease template with information based on the disease diagnosis path, obtain the end node corresponding to the disease template and the user answer path, and generate the user according to the end node and the user answer path. Demand; if the user's demand is a doctor's consultation, the medical data and the user's answer path are input into the preset medical data processing model for disease data processing, and the prediction node is obtained; the prediction node and the end node are compared and analyzed to obtain the comparison result. If the comparison result is that the prediction node and the end node are the same, then the user is matched with a doctor according to the prediction node and the end node, and the target doctor is obtained. This part of the more comprehensive information is used for more accurate doctor-patient matching, which avoids some invalid communication or wrong matching. User self-diagnosis and medical needs identification are carried out according to the disease diagnosis path template, which improves the efficiency of the online consultation process.

The medical data processing method in the embodiment of the present invention has been described above, and the medical data processing device in the embodiment of the present invention is described below. Referring to Fig. 3, the first embodiment of the medical data processing device in the embodiment of the present invention includes:

The acquisition module 301 is used to acquire the medical data corresponding to the user to be processed, and judge whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data;

A matching module 302, configured to match a disease template corresponding to the user according to the medical data if the user conforms to a preset user type, wherein the disease template includes a disease diagnosis path to be filled in by the user;

Filling module 303, for filling the disease template with information based on the disease diagnosis path, obtaining the end node and user answer path corresponding to the disease template, and generating user requirements according to the end node and the user answer path ;

The processing module 304 is configured to input the medical data and the user answer path into a preset medical data processing model for disease data processing if the user demand is a doctor's consultation to obtain a prediction node;

The analysis module 305 is used to compare and analyze the prediction node and the end node to obtain a comparison result;

The generating module 306 is configured to perform doctor matching on the user according to the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same.

Further, the server stores the user requirements in the blockchain database, which is not specifically limited here.

In this embodiment of the present invention, it is determined whether the user conforms to a preset user type according to medical data, wherein the medical data includes user gender, user age and symptom data; if the user conforms to the preset user type, the medical data is matched with the user corresponding to the user disease template, wherein the disease template includes the disease diagnosis path to be filled in by the user; fill the disease template with information based on the disease diagnosis path, obtain the end node corresponding to the disease template and the user answer path, and generate the user according to the end node and the user answer path. Demand; if the user's demand is a doctor's consultation, the medical data and the user's answer path are input into the preset medical data processing model for disease data processing, and the prediction node is obtained; the prediction node and the end node are compared and analyzed to obtain the comparison result. If the comparison result is that the prediction node and the end node are the same, then the user is matched with a doctor according to the prediction node and the end node, and the target doctor is obtained. This part of the more comprehensive information is used for more accurate doctor-patient matching, which avoids some invalid communication or wrong matching. User self-diagnosis and medical needs identification are carried out according to the disease diagnosis path template, which improves the efficiency of the online consultation process.

Referring to FIG. 4, the second embodiment of the medical data processing apparatus in the embodiment of the present invention includes:

The acquisition module 301 is used to acquire the medical data corresponding to the user to be processed, and judge whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data;

A matching module 302, configured to match a disease template corresponding to the user according to the medical data if the user conforms to a preset user type, wherein the disease template includes a disease diagnosis path to be filled in by the user;

Filling module 303, for filling the disease template with information based on the disease diagnosis path, obtaining the end node and user answer path corresponding to the disease template, and generating user requirements according to the end node and the user answer path ;

The processing module 304 is configured to input the medical data and the user answer path into a preset medical data processing model for disease data processing if the user demand is a doctor's consultation to obtain a prediction node;

The analysis module 305 is used to compare and analyze the prediction node and the end node to obtain a comparison result;

The generating module 306 is configured to perform doctor matching on the user according to the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same.

Optionally, the obtaining module 301 is specifically used for:

receiving medical data input by a user to be processed, wherein the medical data includes user gender, user age and symptom data; generating a user portrait corresponding to the user based on the user gender, the user age and the symptom data; Calculate whether the user portrait conforms to the similarity corresponding to the preset user type, and compare the similarity with the preset target value; if the similarity is greater than or equal to the preset target value, determine the The user described above matches the preset user type.

Optionally, the matching module 302 is specifically used for:

If the user conforms to a preset user type, perform keyword extraction on the medical data to obtain a target keyword; respectively match the target keyword with a plurality of preset disease templates to obtain each disease template Corresponding keyword hit rate; sort the keyword hit rate corresponding to each disease template, and use the disease template with the highest keyword hit rate as the disease template corresponding to the user, wherein the disease template includes the user Disease diagnosis path to be filled in.

Optionally, the filling module 303 is specifically used for:

Obtain the pass rate and the recommended click rate based on the disease diagnosis path; fill the disease template with information based on the pass rate and the recommended click rate to obtain a filled disease template; The template performs data extraction to obtain the end node and user answer path corresponding to the disease template; user requirements are generated based on the end node and the user answer path, wherein the user requirements include doctor consultation and offline consultation.

Optionally, the processing module 304 is specifically used for:

If the user demand is a doctor's consultation, the medical data and the user answer path are input into a preset medical data processing model; the medical data and the medical data are processed by the convolutional neural network in the medical data processing model A convolution operation is performed on the user answer path to obtain a feature vector corresponding to the medical data; a feature normalization process is performed on the feature vector to obtain a prediction node.

Optionally, the generating module 306 is specifically used for:

If the comparison result is that the prediction node and the end node are the same, obtain a plurality of candidate doctors in the preset candidate doctor set; based on the prediction node and the end node Reliability calculation to obtain the target reliability corresponding to each candidate doctor; according to the target reliability corresponding to each candidate doctor, the multiple candidate doctors in the candidate doctor set are ranked, and the highest ranked doctor The candidate doctor is used as the doctor matched with the user to obtain the target doctor.

Optionally, the medical data processing device further includes:

A deletion module 307, configured to delete the prediction node if the comparison result is that the prediction node and the end node are different, and generate auxiliary information corresponding to the user based on the disease template; The auxiliary information and the end node perform doctor matching on the user to obtain a target doctor corresponding to the user.

Further, the server stores user requirements in the blockchain database, which is not specifically limited here.

In the embodiment of the present invention, whether the user conforms to a preset user type is determined according to medical data, wherein the medical data includes user gender, user age and symptom data; if the user conforms to the preset user type, the medical data is matched with the user corresponding to the user disease template, wherein the disease template includes the disease diagnosis path to be filled by the user; fill the disease template with information based on the disease diagnosis path, obtain the end node and user answer path corresponding to the disease template, and generate the user according to the end node and the user answer path. Demand; if the user's demand is a doctor's consultation, the medical data and the user's answer path are input into the preset medical data processing model for disease data processing, and the prediction node is obtained; the prediction node and the end node are compared and analyzed to obtain the comparison result. If the comparison result is that the prediction node and the end node are the same, then the user is matched with a doctor according to the prediction node and the end node, and the target doctor is obtained. This part of the more comprehensive information is used for more accurate doctor-patient matching, which avoids some invalid communication or wrong matching. User self-diagnosis and medical needs identification are carried out according to the disease diagnosis path template, which improves the efficiency of the online consultation process.

Figures 3 and 4 above describe the medical data processing apparatus in the embodiment of the present invention in detail from the perspective of modular functional entities, and the following describes the medical data processing device in the embodiment of the present invention in detail from the perspective of hardware processing.

5 is a schematic structural diagram of a medical data processing device according to an embodiment of the present invention. The medical data processing device 500 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units). , CPU) 510 (eg, one or more processors), one or more storage media 530 (eg, one or more mass storage devices) storing application programs 533 or data 532. Among them, the storage medium 530 may be short-term storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), and each module may include a series of instructions to operate on the medical data processing apparatus 500. Further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the medical data processing device 500.

The medical data processing device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 520, and/or, one or more operating systems 531, such as Windows Server , Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the structure of the medical data processing device shown in FIG. 5 does not constitute a limitation on the medical data processing device, and may include more or less components than those shown in the figure, or combine some components, or different Component placement.

The present invention also provides a medical data processing device, the medical data processing device includes a processor, and when the computer readable instructions are executed by the processor, the processor causes the processor to execute the steps of the medical data processing methods in the above embodiments.

The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium may also be a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to execute the steps of the medical data processing method.

Further, the computer-readable storage medium may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required by at least one function, and the like; Use the created data, etc.

The blockchain referred to in the present invention is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

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 this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (random access memory, RAM), magnetic disk or optical disk and other media that can store program codes.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A medical data processing method, wherein the medical data processing method comprises: Obtain medical data corresponding to the user to be processed, and determine whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data; If the user conforms to a preset user type, matching a disease template corresponding to the user according to the medical data, wherein the disease template includes a disease diagnosis path to be filled in by the user; Filling the disease template with information based on the disease diagnosis path, obtaining an end node and a user answer path corresponding to the disease template, and generating user requirements according to the end node and the user answer path; If the user demand is a doctor's consultation, input the medical data and the user's answer path into a preset medical data processing model to process disease data to obtain a prediction node; Carry out comparative analysis on the prediction node and the end node to obtain a comparison result; If the comparison result is that the prediction node and the end node are the same, the user is matched with a doctor according to the prediction node and the end node to obtain a target doctor. 2. The medical data processing method according to claim 1, wherein the obtaining medical data corresponding to the user to be processed, and judging whether the user conforms to a preset user type according to the medical data, comprises: receiving medical data input by a user to be processed, wherein the medical data includes user gender, user age and symptom data; generating a user portrait corresponding to the user based on the user's gender, the user's age, and the symptom data; Calculate whether the user portrait conforms to the similarity corresponding to the preset user type, and compare the similarity with the preset target value; If the similarity is greater than or equal to the preset target value, it is determined that the user conforms to a preset user type. 3. The medical data processing method according to claim 1, wherein if the user conforms to a preset user type, matching a disease template corresponding to the user according to the medical data, comprising: If the user conforms to a preset user type, perform keyword extraction on the medical data to obtain target keywords; Matching the target keyword with a plurality of preset disease templates, respectively, to obtain the keyword hit rate corresponding to each disease template; The keyword hit rate corresponding to each disease template is sorted, and the disease template with the highest keyword hit rate is used as the disease template corresponding to the user, wherein the disease template includes the disease diagnosis path to be filled in by the user. 4. The medical data processing method according to claim 1, wherein the disease template is filled with information based on the disease diagnosis path to obtain an end node and a user answer path corresponding to the disease template, and Generate user requirements according to the end node and the user answer path, including: Obtaining the pass-through rate and the recommended click-through rate based on the disease diagnosis path; Filling the disease template with information based on the run-through rate and the recommended click rate to obtain a filled disease template; performing data extraction on the filled disease template to obtain the corresponding end node and user answer path of the disease template; A user requirement is generated based on the end node and the user answer path, wherein the user requirement includes a doctor's consultation and an offline consultation. 5 . The medical data processing method according to claim 1 , wherein, if the user demand is a doctor’s consultation, the medical data and the user answer path are input into a preset medical data processing model. 6 . Perform disease data processing to obtain prediction nodes, including: If the user demand is a doctor's consultation, input the medical data and the user answer path into a preset medical data processing model; Perform a convolution operation on the medical data and the user answer path through the convolutional neural network in the medical data processing model to obtain a feature vector corresponding to the medical data; The feature normalization process is performed on the feature vector to obtain a prediction node. 6 . The medical data processing method according to claim 1 , wherein, if the comparison result is that the prediction node and the end node are the same, then the prediction node and the end node are paired according to the comparison result. 7 . The user performs doctor matching to obtain the target doctor, including: If the comparison result is that the prediction node and the end node are the same, acquiring multiple candidate doctors in the preset candidate doctor set; Perform credibility calculation on the plurality of candidate doctors based on the prediction node and the end node, to obtain the target credibility corresponding to each candidate doctor; Rank the multiple candidate doctors in the candidate doctor set according to the target reliability corresponding to each candidate doctor, and use the highest ranked candidate doctor as the doctor matching the user to obtain the target doctor. 7. The medical data processing method according to any one of claims 1-6, characterized in that, if the comparison result is that the prediction node and the end node are the same, then according to the prediction The node and the end node perform doctor matching on the user, and after obtaining the target doctor, the medical data processing method further includes: If the comparison result is that the prediction node and the end node are different, delete the prediction node, and generate auxiliary information corresponding to the user based on the disease template; Doctor matching is performed on the user based on the auxiliary information and the end node, and a target doctor corresponding to the user is obtained. 8. A medical data processing device, wherein the medical data processing device comprises: an acquisition module, configured to acquire medical data corresponding to the user to be processed, and determine whether the user conforms to a preset user type according to the medical data, wherein the medical data includes user gender, user age and symptom data; a matching module, configured to match a disease template corresponding to the user according to the medical data if the user conforms to a preset user type, wherein the disease template includes a disease diagnosis path to be filled in by the user; a filling module, configured to fill the disease template with information based on the disease diagnosis path, obtain an end node and a user answer path corresponding to the disease template, and generate user requirements according to the end node and the user answer path; a processing module, configured to input the medical data and the user answer path into a preset medical data processing model for disease data processing to obtain a prediction node if the user demand is a doctor's consultation; an analysis module, used to compare and analyze the prediction node and the end node to obtain a comparison result; A generating module, configured to perform doctor matching on the user according to the prediction node and the end node to obtain a target doctor if the comparison result is that the prediction node and the end node are the same. 9. A medical data processing device, characterized in that the medical data processing device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor invokes the instructions in the memory to cause the medical data processing apparatus to perform the medical data processing method of any one of claims 1-7. 10. A computer-readable storage medium on which instructions are stored, wherein the instructions are executed by a processor to realize the medical data according to any one of claims 1-7 Approach.

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