Inquiry user loss prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the application provides a kind of diagnosis user loss prediction method, device, electronic equipment and storage medium, belong to digital medical field.The method comprises: obtaining user characteristic index, the user characteristic index is used to indicate the diagnosis behavior of user;The user characteristic index is input into loss prediction model, and first loss prediction result is obtained;Obtain the disease label of the user, and the disease label is used to indicate the re-visit demand of the user;According to the disease label and the first loss prediction result, determine the target loss prediction result of the user.The application can improve the loss prediction accuracy of diagnosis user.

Description

Technical Field

[0001] The present application relates to the field of digital medicine, and in particular to a method, device, electronic device and storage medium for predicting user churn during medical consultation. Background Art

[0002] With the rise of Internet medical technology, online medical consultation has gradually become one of the main ways for people to see a doctor. In the competition among various corporate Internet platforms, how to improve user loyalty to their platforms and prevent user churn has become a crucial link.

[0003] In related technologies, user churn of a platform is mostly predicted using a churn prediction model, which directly outputs a prediction result. However, the accuracy of user churn prediction directly performed using a churn prediction model is currently low. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a method, device, electronic device and storage medium for predicting churn of consultation users, aiming to improve the accuracy of churn prediction for consultation users.

[0005] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for predicting user churn after a medical consultation, the method comprising:

[0006] Obtaining a user characteristic index, wherein the user characteristic index is used to indicate the user's medical consultation behavior;

[0007] Inputting the user characteristic indicator into a churn prediction model to obtain a first churn prediction result;

[0008] Obtaining a disease tag of the user, where the disease tag is used to indicate the user's need for a follow-up consultation;

[0009] A target churn prediction result for the user is determined based on the disease label and the first churn prediction result.

[0010] In some embodiments, the disease tag indicates that the user needs to undergo a follow-up consultation;

[0011] Determining the target churn prediction result of the user according to the disease label and the first churn prediction result includes:

[0012] Obtaining the user's follow-up visit behavior data, wherein the follow-up visit behavior data is used to indicate whether the user has had a follow-up visit within the follow-up visit period;

[0013] If the user has a follow-up consultation within the follow-up consultation period, using the first churn prediction result as the target churn prediction result for the user;

[0014] In a case where the user does not have a follow-up consultation within the follow-up consultation period, a target churn prediction result for the user is determined based on the first churn prediction result.

[0015] In some embodiments, determining a target churn prediction result for the user based on the first churn prediction result includes:

[0016] In a case where the first churn prediction result predicts churn of the user, using the first churn prediction result as a target churn prediction result for the user;

[0017] In a case where the first churn prediction result predicts that the user has not churned, the first churn prediction result is corrected to obtain a second churn prediction result, and the second churn prediction result is used as a target churn prediction result.

[0018] In some embodiments, the disease label indicates that the user does not need a follow-up visit;

[0019] Determining the target churn prediction result of the user according to the disease label and the first churn prediction result includes:

[0020] The first churn prediction result is used as the target churn prediction result.

[0021] In some embodiments, obtaining the disease label of the user includes:

[0022] Obtain a follow-up prediction model based on big data learning;

[0023] Acquiring the user's diagnosis and treatment data, where the diagnosis and treatment data is used to indicate the user's disease information;

[0024] The user's diagnosis and treatment data is input into the re-examination prediction model to obtain the user's disease label.

[0025] In some embodiments, obtaining a user characteristic indicator includes:

[0026] Get user ID data;

[0027] Obtain the user consultation behavior data corresponding to each user ID based on the user ID data;

[0028] The user consultation behavior data corresponding to each user ID is used as a user feature indicator.

[0029] In some embodiments, the churn prediction model is a decision tree model;

[0030] Before inputting the user characteristic indicator into the churn prediction model to obtain the first churn prediction result, the method further includes:

[0031] Obtain a user history medical inquiry dataset including multiple user history medical inquiry features;

[0032] Calculating the information gain rate of each of the user's historical medical inquiry features, performing feature selection based on the information gain rate, and obtaining a feature selection result;

[0033] Build a decision tree model based on the feature selection results.

[0034] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a device for predicting user churn after a medical consultation, the device comprising:

[0035] A user feature acquisition module, used to acquire user feature indicators, wherein the user feature indicators are used to indicate the user's medical consultation behavior;

[0036] A first churn prediction module, configured to perform churn prediction processing based on the user characteristic indicators to obtain a first churn prediction result;

[0037] A disease label acquisition module, configured to acquire the user's disease label, wherein the disease label is used to indicate the user's need for a follow-up consultation;

[0038] The second churn prediction module is used to determine a target churn prediction result for the user based on the disease label and the first churn prediction result.

[0039] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the above-mentioned method for predicting user churn are implemented.

[0040] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the above-mentioned method for predicting user churn.

[0041] The present application proposes a method, device, electronic device, and storage medium for predicting churn among users seeking medical consultations. The method obtains a user characteristic indicator, which indicates a user's medical consultation behavior; inputs the user characteristic indicator into a churn prediction model to obtain a first churn prediction result; obtains a disease label for the user, which indicates the user's need for a follow-up visit; and determines a target churn prediction result for the user based on the disease label and the first churn prediction result. The present application combines the disease label and the first churn prediction result to predict the user's churn probability, thereby improving the accuracy of churn prediction for users seeking medical consultations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of a method for predicting user churn in consultations provided by an embodiment of the present application;

[0043] Figure 2 yes Figure 1 Flowchart of step S104;

[0044] Figure 3 yes Figure 2 Flowchart of step S203;

[0045] Figure 4 When the user does not need a follow-up visit Figure 1 Flowchart of step S104;

[0046] Figure 5 yes Figure 1 Flowchart of step S103;

[0047] Figure 6 yes Figure 1 Flowchart of step S101 in FIG.

[0048] Figure 7 This is a flowchart of constructing a decision tree model provided by an embodiment of the present application;

[0049] Figure 8 This is a schematic diagram of the structure of the device for predicting user churn in consultation provided by an embodiment of the present application;

[0050] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0054] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0055] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0056] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0057] First, let’s analyze some of the terms used in this application:

[0058] Artificial Intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and create new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or digital computer-controlled machines to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0059] Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese and English). It is a branch of artificial intelligence and an interdisciplinary field between computer science and linguistics, often referred to as computational linguistics. Natural language processing encompasses grammatical analysis, semantic analysis, and discourse comprehension. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent identification, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It encompasses data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research related to language processing, and linguistics research related to language computing.

[0060] Information Extraction (I nformation Extraction) is a text processing technology that extracts specified types of entity, relationship, event, and other factual information from natural language text and forms structured data output. Information extraction is a technology that extracts specific information from text data. Text data is composed of specific units, such as sentences, paragraphs, and chapters. Text information is composed of small specific units, such as characters, words, phrases, sentences, paragraphs, or combinations of these specific units. Extracting noun phrases, names, and place names from text data is all text information extraction. Of course, the information extracted by text information extraction technology can be of various types.

[0061] Churn prediction modeling is a data analysis tool used to predict the likelihood that a customer or user will leave or stop using a product or service within a certain period of time. In many industries, such as telecommunications, banking, subscription services, and e-commerce, customer churn is a key factor affecting revenue and growth. By predicting which customers are likely to churn, companies can take targeted measures to reduce churn and improve customer retention, thereby optimizing marketing strategies and boosting customer satisfaction.

[0062] With the rise of Internet medical technology, online medical consultation has gradually become one of the main ways for people to see a doctor. In the competition among various corporate Internet platforms, how to improve user loyalty to their platforms and prevent user churn has become a crucial link.

[0063] In related technologies, user churn of a platform is mostly predicted using a churn prediction model, which directly outputs a prediction result. However, the accuracy of user churn prediction directly performed using a churn prediction model is currently low.

[0064] Based on this, the embodiments of the present application provide a method, device, electronic device and storage medium for predicting user churn based on a medical consultation, aiming to improve the accuracy of user churn prediction.

[0065] The method, device, electronic device and storage medium for predicting user churn in consultation provided by the embodiments of the present application are specifically illustrated through the following embodiments. First, the method for predicting user churn in consultation provided in the embodiments of the present application is described.

[0066] The present application proposes a method, device, electronic device, and storage medium for predicting churn among users seeking medical consultations. The method obtains a user characteristic indicator, which indicates a user's medical consultation behavior; inputs the user characteristic indicator into a churn prediction model to obtain a first churn prediction result; obtains a disease label for the user, which indicates the user's need for a follow-up visit; and determines a target churn prediction result for the user based on the disease label and the first churn prediction result. The present application combines the disease label and the first churn prediction result to predict the user's churn probability, thereby improving the accuracy of churn prediction for users seeking medical consultations.

[0067] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0068] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0069] The method for predicting the churn of medical consultation users provided in the embodiment of the present application relates to the field of artificial intelligence technology. The method for predicting the churn of medical consultation users provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for predicting the churn of medical consultation users, etc., but is not limited to the above forms.

[0070] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0071] Figure 1 This is an optional flowchart of the method for predicting user churn provided by the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0072] Step S101: Obtain user characteristic indicators, which are used to indicate the user's medical consultation behavior;

[0073] Step S102: Input the user characteristic index into the churn prediction model to obtain a first churn prediction result;

[0074] Step S103: Obtain the user's disease tag, which is used to indicate the user's need for a follow-up consultation;

[0075] Step S104: Determine a target churn prediction result for the user based on the disease label and the first churn prediction result.

[0076] Steps S101 to S104 of this embodiment of the present application improve the accuracy of churn prediction for users by obtaining user feature indicators, inputting the user feature indicators into a churn prediction model to obtain a first churn prediction result, obtaining the user's disease label, and determining a target churn prediction result for the user based on the disease label and the first churn prediction result.

[0077] In step S101 of some embodiments, the user characteristic indicators are first explained. The user characteristic indicators are a series of data points used in user churn prediction (Churn Prediction) to analyze and predict whether a user will leave a product, service or platform. In the embodiment of the present application, the user's consultation behavior is used as a user characteristic indicator, that is, the user's various consultation behaviors are used as data points for churn prediction to facilitate subsequent churn prediction. Exemplarily, the user characteristic indicators in the embodiment of the present application include but are not limited to the number of consultations of the user, the consultation time period, the major category of the consultation department, the proposed diagnosis label, whether a prescription is issued, whether payment is made, the classification of drugs, the major product category to which non-drug goods belong, the average customer price, etc.

[0078] It should be noted that the various types of medical consultation behavior data included in the user characteristic indicators can be obtained through the user's medical data, through the user's natural language text, and can also be obtained through screening by artificial intelligence models.

[0079] In one possible implementation, the medical data may be personal health records, prescriptions, examination reports, and other data.

[0080] In one possible implementation, the natural language text is a medical text, which may be an electronic medical record (Electronic Healthcare Record), an electronic personal health record, including a series of electronic records with preservation value such as medical records, electrocardiograms, and medical images.

[0081] In one possible implementation, various types of user consultation behaviors can be screened out from online conversations between users and doctors based on an artificial intelligence model.

[0082] In some embodiments, step S102 of inputting user characteristic indicators into a churn prediction model refers to using the user characteristic indicators as input to obtain a preliminary prediction result of user churn, i.e., a first churn prediction result, through the churn prediction model. The churn prediction model may employ a decision tree model, a random forest model, a neural network model, or the like.

[0083] For example, the process of churn prediction using a decision tree model is as follows:

[0084] First, the user characteristic index obtained in step S101 is subjected to min-max normalization processing to achieve dimensionless normalization of the data.

[0085] The standardized user feature index data is then input into the decision tree model, and feature selection is performed on each feature to find the optimal feature subset, thereby obtaining the first churn prediction result. In the embodiment of the present application, the C4.5 decision tree algorithm is used to select features. The C4.5 decision tree algorithm performs feature selection based on the information gain rate. The information gain calculation formula of feature A for data set D is: Gain(A) = Info(D) - Info_A(D), where data set D is a set of user feature indicators and feature A is any feature in the user feature indicators.

[0086] The information gain rate (Informat i on Gain Rate) is used to measure the ratio of the amount of information brought by an attribute when dividing a data set relative to the inherent uncertainty of the attribute. The information gain rate is obtained by dividing the information gain by the inherent value. Its purpose is to standardize the information gain to prevent bias towards attributes with more values. An attribute with a higher information gain rate means that even taking into account the uncertainty of the attribute itself, it can still provide a relatively high information gain, that is, its contribution to classification is significant relative to its inherent uncertainty. The information gain rate is calculated as follows: Gainr(A) = Gain(A) / H(A). Among them, H(A) is the entropy of A, and entropy is used to measure the uncertainty of samples in a data set. The higher the entropy, the greater the uncertainty. The calculation formula of H(A) is as follows:

[0087]

[0088] In the above formula, n is the number of categories of feature A, D j is the number of samples corresponding to the i-th value of feature A, and |D| is the number of samples.

[0089] The above is an example of obtaining the first churn prediction result according to the decision tree model based on the C4.5 decision tree algorithm in this application. In addition to the C4.5 decision tree algorithm, decision tree models constructed using the ID3 and CART decision tree algorithms can also be used.

[0090] In step S103 of some embodiments, the disease tag refers to a tag assigned to each user related to a follow-up visit. The disease tag can be used to directly determine whether the user needs a follow-up visit, as well as follow-up deadlines and other information related to the follow-up visit. The disease tag can be assigned directly by the attending physician based on the user's physical condition, disease type, or medication frequency, or it can be automatically assigned by the system after identifying the user's medical data and medical text using big data or artificial intelligence.

[0091] In one possible implementation, the medical data may be personal health records, prescriptions, examination reports, and other data.

[0092] In one possible implementation, the natural language text is a medical text, which may be an electronic medical record (Electronic Healthcare Record), an electronic personal health record, including a series of electronic records with preservation value such as medical records, electrocardiograms, and medical images.

[0093] In step S104 of some embodiments, the target churn prediction result refers to the final churn prediction conclusion. The target churn prediction result of the user is determined based on the disease label and the first churn prediction result. It means that the user churn is predicted based on the need for follow-up visits in the disease label first. If the user does not have a follow-up visit within the follow-up visit period, the user churn is directly judged as lost regardless of the first churn prediction result. At this time, regardless of the first churn prediction result, the user's target churn prediction result is user churn. That is, the user churn situation is determined based on the user's disease label first. If the disease label shows that the user has no need for follow-up visits or the user has no disease label, which is not enough to determine whether the user has a follow-up visit or not, the first churn prediction result is used as the target churn prediction result, replacing the traditional method of directly predicting based on the model results, which can improve the accuracy of churn prediction for consulting users.

[0094] See also Figure 2 , Figure 2 This is a flowchart of the specific method of step S104 in some embodiments of the present application. In some embodiments of the present application, when the disease label indicates that the user has a need for a follow-up visit, step S104 specifically includes but is not limited to steps S201 to S203. Figure 2 These three steps are described in detail:

[0095] Step S201: Acquire the user's follow-up visit behavior data, where the follow-up visit behavior data is used to indicate whether the user has had a follow-up visit within the follow-up visit period;

[0096] Step S202: If the user has a follow-up consultation within the follow-up consultation period, use the first churn prediction result as the target churn prediction result for the user;

[0097] Step S203: If the user does not have a follow-up consultation within the follow-up consultation period, determine a target churn prediction result for the user based on the first churn prediction result.

[0098] In steps S201 to S203 of the embodiment of the present application, when the disease tag indicates that the user needs a follow-up visit, the user's follow-up visit behavior data is obtained; if the user has a follow-up visit within the follow-up visit period, the first churn prediction result is used as the user's target churn prediction result; if the user has not had a follow-up visit within the follow-up visit period, the user's target churn prediction result is determined based on the first churn prediction result. This can further improve the accuracy of user churn prediction.

[0099] In step S201 of some embodiments, obtaining the user's follow-up behavior data means first determining whether the user has a follow-up need based on the disease label. If so, querying the user's historical consultation records based on the follow-up period in the disease label to determine whether the user has had a follow-up within the follow-up period.

[0100] It should be noted that the user's follow-up behavior data can be obtained directly by querying the user's registration / consultation records during the follow-up period, or it can be given after analyzing the user's medical data or medical text through artificial intelligence.

[0101] In step S202 of some embodiments, when the user has a follow-up visit within the follow-up period, the first churn prediction result is used as the target churn prediction result of the user. This means that when the user's follow-up behavior data shows that the user has a follow-up visit within the follow-up period, and it is impossible to determine whether the user has churned based on the disease label, the first churn prediction result is used as the target churn prediction result of the user, that is, the output result of the churn prediction model is used as the final churn prediction conclusion.

[0102] In step S203 of some embodiments, when the user does not have a follow-up visit within the follow-up period, the user's target churn prediction result is determined based on the first churn prediction result, which means that when the user's follow-up behavior data shows that the user has not had a follow-up visit within the follow-up period, the first churn prediction result is combined as the user's target churn prediction result. It should be understood that at this time, the user's target churn prediction result is to judge that the user has churned, that is, the conclusion of user churn is used as the final churn prediction conclusion.

[0103] See also Figure 3 , Figure 3This is a flowchart of the specific method of step S203 in some embodiments of the present application. In some embodiments of the present application, step S203 specifically includes but is not limited to step S301 and step S302. Figure 3 These two steps are described in detail:

[0104] S301. When a first churn prediction result predicts user churn, use the first churn prediction result as a target churn prediction result for the user;

[0105] S302: If the first churn prediction result predicts that the user has not churned, modify the first churn prediction result to obtain a second churn prediction result, and use the second churn prediction result as a target churn prediction result.

[0106] In steps S201 to S202 of the embodiment of the present application, when the first churn prediction result predicts user churn, the first churn prediction result is used as the target churn prediction result for the user; and when the first churn prediction result predicts no user churn, the first churn prediction result is corrected to obtain a second churn prediction result, and the second churn prediction result is used as the target churn prediction result. This can further improve the accuracy of user churn prediction.

[0107] In step S301 of some embodiments, when the first churn prediction result predicts user churn, the first churn prediction result is used as the target churn prediction result for the user; this means that if the follow-up history data shows that the user has not had a follow-up visit within the follow-up period, the prediction result based on the disease label should be user churn, and at this time, if the first churn prediction result obtained according to the churn prediction model also predicts user churn, there is no need to correct the first churn prediction result, and the first churn prediction result can be directly used as the target churn prediction result, because at this time the prediction results of the two methods are consistent, that is, the first churn prediction result is the same as the prediction result based on the disease label and the follow-up history data, both of which judge user churn, and at this time the target churn prediction result is user churn.

[0108] In step S302 of some embodiments, when the first churn prediction result predicts that the user has not churned, the first churn prediction result is corrected to obtain a second churn prediction result, and the second churn prediction result is used as the target churn prediction result. The second churn prediction result means that if the follow-up history data shows that the user has not had a follow-up visit within the follow-up period, the prediction result based on the disease label should be user churn, but at this time the first churn prediction result predicts that the user has not churned. Because the prediction results of the two methods are inconsistent at this time, the first churn prediction result needs to be corrected. Obviously, when the user has not had a follow-up visit within the follow-up period, it can be concluded that the user has churned. At this time, the first churn prediction result obtained by the churn prediction model is wrong, so the first churn prediction result is corrected, and the prediction result based on the disease label is used as the second churn prediction result. At this time, the target churn prediction result is user churn.

[0109] See also Figure 4 , Figure 4 This is a flowchart of the specific method of step S104 in some embodiments of the present application. In some embodiments of the present application, when the disease label indicates that the user does not need a follow-up visit, step S104 specifically includes but is not limited to step S401. Figure 4 This step is described in detail:

[0110] Step S401: Use the first churn prediction result as the target churn prediction result.

[0111] In step S401 of the embodiment of the present application, when the disease tag indicates that the user does not need a follow-up visit, the first churn prediction result is used as the target churn prediction result, which can reduce the program processing flow and save computing resources.

[0112] In some embodiments, in step S401, if the disease tag indicates that the user does not need a follow-up visit, the first churn prediction result output by the churn prediction model is directly used as the target churn prediction result. That is, if the user does not need a follow-up visit, it is impossible to determine the user's churn status based on whether or not they have a follow-up visit. Therefore, no relevant judgment or prediction is performed, and the first churn prediction result output by the churn prediction model is directly used as the final churn prediction conclusion.

[0113] See also Figure 5 , Figure 5 This is a flowchart of the specific method of step S103 in some embodiments of the present application. In some embodiments of the present application, step S103 specifically includes but is not limited to steps S501 to S503. Figure 5 These three steps are described in detail:

[0114] Step S501: Obtain a follow-up visit prediction model based on big data learning;

[0115] Step S502: Obtain the user's medical data, which is used to indicate the user's disease information;

[0116] Step S503: Input the user's diagnosis and treatment data into the follow-up prediction model to obtain the user's disease label.

[0117] In steps S501 to S503 of the present embodiment, a return visit prediction model based on big data learning is used to obtain the user's medical data, which indicates the user's disease information. The user's medical data is then input into the return visit prediction model to obtain the user's disease label. This automatically generates the user's disease label, eliminating the need for the doctor to manually add it, saving the doctor's consultation time and improving consultation efficiency.

[0118] In step S501 of some embodiments, the return visit prediction model based on big data learning refers to the ability to predict return visit-related content such as whether the user has a return visit behavior and the return visit deadline based on the user's medical data. The return visit prediction model based on big data learning can adopt models such as logistic regression, random forest, support vector machine, gradient boosting tree, neural network, etc. The return visit prediction model can adopt existing related models or be constructed according to needs.

[0119] As an example, the following is a process for constructing a follow-up visit prediction model:

[0120] The historical diagnosis and treatment data of multiple users are obtained to form a historical diagnosis and treatment data set; in one possible implementation, the historical diagnosis and treatment data may be personal health records, prescriptions, examination reports and other data; in one possible implementation, the historical diagnosis and treatment data may be electronic medical records (Electronic Healthcare Record), electronic personal health records, including medical records, electrocardiograms, medical images and other electronic records with preservation value.

[0121] It should be noted that the multiple users include users who have a need for a follow-up visit and users who do not have a need for a follow-up visit.

[0122] Then, the historical diagnosis and treatment dataset is preprocessed and divided into training set, validation set and test set;

[0123] Define the features associated with follow-up needs in the dataset and build a preliminary neural network model based on the relevant features;

[0124] The preliminary neural network model is trained using the training set, validation set, and test set, and finally a trained revisit prediction model is obtained.

[0125] It should be noted that the above is only a process for constructing a return visit prediction model based on a neural network. This application can select different algorithms to construct a return visit prediction model according to actual needs.

[0126] In step S502 of some embodiments, the user's medical data is obtained. The medical data may be the user's specific symptoms, physical condition, medication frequency, and drug information, etc., which can reflect the user's disease information.

[0127] For example, if a user has a specific chronic disease, or based on the historical medication history collected, it is found that the user needs regular follow-up visits and re-prescriptions for medication, these can be used as information on whether the user needs follow-up visits; in addition to the above diagnosis and treatment data, some users in special periods also need regular follow-up visits, for example, if the past history includes respiratory diseases, mental diseases, immune system diseases, urinary system diseases, peri-pregnancy period, perinatal period, etc., these are all user's diagnosis and treatment data.

[0128] In one possible implementation, the diagnosis and treatment data may be personal health records, prescriptions, examination reports, and other data;

[0129] In one possible implementation, the diagnosis and treatment data may be an electronic medical record (Electronic Health Care Record), an electronic personal health record, including medical records, electrocardiograms, medical images, and other electronic records that are valuable for preservation and future reference.

[0130] In some embodiments, step S503 inputs the user's medical data into a follow-up visit prediction model to obtain the user's disease tag. This involves using the medical data acquired in step S502 as input and using the follow-up visit prediction model to predict whether the user needs a follow-up visit and, if so, when the follow-up visit will be necessary. By generating a user's disease tag through the big data-based follow-up visit prediction model, the doctor no longer needs to manually add the tag, saving the doctor's consultation time and improving consultation efficiency.

[0131] See also Figure 6 , Figure 6 This is a flowchart of the specific method of step S101 in some embodiments of the present application. In some embodiments of the present application, step S101 specifically includes but is not limited to steps S601 to S603. Figure 6 These three steps are described in detail:

[0132] Step S601: Obtain user ID data;

[0133] Step S602: Obtain user consultation behavior data corresponding to each user ID based on the user ID data;

[0134] Step S603: Use the user consultation behavior data corresponding to each user ID as a user characteristic indicator.

[0135] In steps S601 to S603, as shown in the embodiment of this application, user ID data is obtained, and then the user consultation behavior data corresponding to each user ID is obtained based on the user ID data, and the user consultation behavior data corresponding to each user ID is used as a user feature indicator. Based on the particularity of Internet medical consultation users, this application eliminates the basic user attributes used in traditional prediction when selecting user features, and uses the user ID as an independent churn prediction object, thereby reducing the possibility of deviation in the prediction results of the churn prediction model and further improving the accuracy of churn prediction.

[0136] In step S601 of some embodiments, obtaining user ID data refers to obtaining a unique identifier corresponding to each user ID of the Internet medical platform. The unique identifier can be the nickname or login name of the user ID, the mobile phone number or email address bound to the user ID, or the UID number corresponding to the user ID in the background database, etc.

[0137] It should be understood that a nickname can be selected as the unique identifier of a user ID only when users on the Internet platform are not allowed to have the same user nickname.

[0138] In step S602 of some embodiments, the user consultation behavior data corresponding to each user ID is obtained based on the user ID data, which means that no matter whether the user ID is associated with one or several patients, all the consultation behavior data under each user ID are counted only in units of user ID, without distinguishing whether the user ID corresponds to one or several patients.

[0139] In one possible implementation, the consultation behavior data may be all personal health records, prescriptions, examination reports, and other data under the user ID;

[0140] In one possible implementation, the consultation behavior data can be all electronic medical records (Electronic Healthcare Record) under the user ID, electronic personal health records, including medical records, electrocardiograms, medical images, and other electronic records with preservation value.

[0141] In step S603 of some embodiments, due to the particularity of online medical consultations on the Internet, multiple patients may be bound to one user ID. In actual consultations, a single user ID may consult multiple patients, and these multiple patients are usually family members of the registered user ID. Since some Internet medical platforms do not have mandatory identity authentication, the consulting doctor and the system cannot distinguish which patient is conducting a consultation, and the basic attributes of each patient may be very different.

[0142] Therefore, in the embodiments of the present application, the user consultation behavior data corresponding to each user ID is used as a user feature indicator. This means that when selecting user features, the traditional selection of basic user attributes is abandoned, reducing the impact on the prediction results. In other words, when making predictions using the churn prediction model, the user ID is used as an independent individual. Regardless of whether the user ID corresponds to one or more patients, only the churn probability of each user ID is predicted. This can improve the accuracy of the first churn prediction result output by the churn prediction model.

[0143] See also Figure 7 In the embodiment of the present application, the churn prediction model is a decision tree model. In some embodiments, before step S202, the method for predicting user churn may also include but is not limited to steps S701 to S703. Figure 7 These three steps are described in detail:

[0144] Step S701: obtaining a user history medical inquiry dataset including multiple user history medical inquiry features;

[0145] Step S702: Calculate the information gain rate of each user's historical medical inquiry features, perform feature selection based on the information gain rate, and obtain a feature selection result;

[0146] Step S703: Construct a decision tree model based on the feature selection results.

[0147] In steps S701 to S703 of the embodiment of the present application, a user history inquiry dataset including multiple user history inquiry features is obtained, the information gain ratio of each user history inquiry feature is calculated, and feature selection is performed based on the information gain ratio to obtain a feature selection result. A decision tree model is then constructed based on the feature selection result. This results in a decision tree model that matches the user characteristics of online medical consultations, further improving the accuracy of the first churn prediction result.

[0148] In step S701 of some embodiments, the user's historical medical consultation features refer to all historical medical consultation behaviors and records of churn of the user. All historical medical consultation behaviors and records of churn of multiple users are collected to form a user historical medical consultation data set. Historical medical consultation behaviors include but are not limited to collecting users' medical records, including medical history, diagnosis, treatment, drug use, laboratory test results, doctor's advice, and patients' personal information. Personal information includes age, gender, lifestyle, etc. It should be understood that the more users there are in the user's historical medical consultation data set, the more accurate the prediction results that can be obtained by the constructed decision tree model. The user's historical medical consultation features can be obtained through the user's medical data, through the user's natural language text, or through screening and acquisition by an artificial intelligence model.

[0149] In one possible implementation, the medical data may be personal health records, prescriptions, examination reports, and other data.

[0150] In one possible implementation, the natural language text is a medical text, which may be an electronic medical record (Electronic Healthcare Record), an electronic personal health record, including a series of electronic records with preservation value such as medical records, electrocardiograms, and medical images.

[0151] In one possible implementation, various historical medical consultation features of the user can be filtered out from the historical conversation records between the user and the doctor based on an artificial intelligence model.

[0152] In step S702 of some embodiments, the information gain rate of each user's historical medical inquiry feature is calculated, and feature selection is performed based on the information gain rate to obtain a feature selection result. This means that the information gain rate of each feature in the user's historical medical inquiry feature is first calculated, and the feature with the highest information gain rate is selected from all features as the root node of the decision tree. The relevant content of the information gain rate has been introduced in detail in the previous text and will not be described in detail here.

[0153] In step S703 of some embodiments, building a decision tree model based on the feature selection results refers to calculating the information gain rate of each feature in step S702, obtaining a feature selection result, creating a child node for each feature value, splitting the data set according to the different values ​​of the feature, and repeating the above process for each child node until the stopping condition is met, such as the number of samples in the node is lower than a certain threshold or all samples belong to the same category, and then building a training set and a data set based on the user's historical consultation data set, using the training set and the data set to build a decision tree model through a recursive splitting and pruning process, using the training data set to train the decision tree model, and the decision tree model makes branch decisions based on the feature values, and finally achieves the prediction target. Since all user historical consultation features are derived from online consultations on the Internet medical platform, a decision tree model that matches the user features of the Internet medical online consultation can be obtained through the above method, which conforms to the particularity of the consultation business and can further improve the accuracy of the first churn prediction result.

[0154] The present application also provides a device for predicting user churn. Figure 8 , which can implement the above-mentioned consultation user churn prediction method, the device includes: a user feature acquisition module 801, a first churn prediction module 802, a disease label acquisition module 803 and a second churn prediction module 804.

[0155] The user feature acquisition module 801 is used to acquire user feature indicators, which are used to indicate the user's medical consultation behavior;

[0156] A first churn prediction module 802 is configured to perform churn prediction processing based on user characteristic indicators to obtain a first churn prediction result;

[0157] The disease label acquisition module 803 is used to obtain the user's disease label, which is used to indicate the user's need for a follow-up visit;

[0158] The second churn prediction module 804 is configured to determine a target churn prediction result for the user based on the disease label and the first churn prediction result.

[0159] The medical consultation user churn prediction device of the embodiment of the present application is used to execute the medical consultation user churn prediction method in the above embodiment. Its specific processing process is the same as the medical consultation user churn prediction method in the above embodiment, and will not be repeated here.

[0160] The present application also provides an electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, the above-described method for predicting user churn is implemented. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0161] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0162] The processor 901 may be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0163] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the method for predicting user churn in the embodiment of this application.

[0164] Input / output interface 903, used to implement information input and output;

[0165] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.);

[0166] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0167] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0168] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned consultation user churn prediction method.

[0169] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0170] The present application proposes a method, device, electronic device, and storage medium for predicting churn among users seeking medical consultations. The method obtains a user characteristic indicator, which indicates a user's medical consultation behavior; inputs the user characteristic indicator into a churn prediction model to obtain a first churn prediction result; obtains a disease label for the user, which indicates the user's need for a follow-up visit; and determines a target churn prediction result for the user based on the disease label and the first churn prediction result. The present application combines the disease label and the first churn prediction result to predict the user's churn probability, thereby improving the accuracy of churn prediction for users seeking medical consultations.

[0171] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0172] It will be understood by those skilled in the art that Figure 1-7 The technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or a combination of certain steps, or different steps.

[0173] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0174] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0175] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0176] It should be understood that in this application, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can indicate: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can indicate: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0180] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application 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 multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0181] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for predicting user churn in a medical consultation, characterized in that: The method comprises: Obtaining a user characteristic index, wherein the user characteristic index is used to indicate the user's medical consultation behavior; Inputting the user characteristic indicator into a churn prediction model to obtain a first churn prediction result; Obtaining a disease tag of the user, where the disease tag is used to indicate the user's need for a follow-up consultation; Determining a target churn prediction result for the user based on the disease label and the first churn prediction result; Wherein, when the disease label indicates that the user has a need for a follow-up visit, obtaining the user's follow-up visit behavior data, the follow-up visit behavior data is used to indicate whether the user has had a follow-up visit within the follow-up visit period; when the user has not had a follow-up visit within the follow-up visit period and the first churn prediction result predicts that the user has not churned, revising the first churn prediction result to obtain a second churn prediction result, and using the second churn prediction result as the target churn prediction result; When the disease label indicates that the user does not need a follow-up visit, the first churn prediction result is used as a target churn prediction result.

2. The method according to claim 1, characterized in that The disease label indicates that the user needs a follow-up consultation; The determining, based on the disease label and the first churn prediction result, a target churn prediction result for the user further includes: In a case where the user has a follow-up consultation within the follow-up consultation period, the first churn prediction result is used as the target churn prediction result for the user.

3. The method according to claim 1, characterized in that The disease label indicates that the user has a need for a follow-up visit; determining the target churn prediction result of the user based on the disease label and the first churn prediction result also includes: if the user does not have a follow-up visit within the follow-up period and the first churn prediction result predicts that the user will churn, using the first churn prediction result as the target churn prediction result of the user.

4. The method according to claim 1, wherein The obtaining of the disease label of the user includes: Obtain a follow-up prediction model based on big data learning; Acquiring the user's diagnosis and treatment data, where the diagnosis and treatment data is used to indicate the user's disease information; The user's diagnosis and treatment data is input into the re-examination prediction model to obtain the user's disease label.

5. The method according to claim 1, wherein The obtaining of user characteristic indicators includes: Get user ID data; Obtain the user consultation behavior data corresponding to each user ID based on the user ID data; The user consultation behavior data corresponding to each user ID is used as a user feature indicator.

6. The method according to claim 1, characterized in that The churn prediction model is a decision tree model; Before inputting the user characteristic indicator into the churn prediction model to obtain the first churn prediction result, the method further includes: Obtain a user history medical inquiry dataset including multiple user history medical inquiry features; Calculating the information gain rate of each of the user's historical medical inquiry features, performing feature selection based on the information gain rate, and obtaining a feature selection result; Build a decision tree model based on the feature selection results.

7. A device for predicting user churn in a medical consultation, characterized in that: The device comprises: A user feature acquisition module, used to acquire user feature indicators, wherein the user feature indicators are used to indicate the user's medical consultation behavior; A first churn prediction module, configured to perform churn prediction processing based on the user characteristic indicators to obtain a first churn prediction result; A disease label acquisition module, configured to acquire the user's disease label, wherein the disease label is used to indicate the user's need for a follow-up consultation; a second churn prediction module, configured to determine a target churn prediction result for the user based on the disease label and the first churn prediction result; Wherein, when the disease label indicates that the user has a need for a follow-up visit, obtaining the user's follow-up visit behavior data, the follow-up visit behavior data is used to indicate whether the user has had a follow-up visit within the follow-up visit period; when the user has not had a follow-up visit within the follow-up visit period and the first churn prediction result predicts that the user has not churned, revising the first churn prediction result to obtain a second churn prediction result, and using the second churn prediction result as the target churn prediction result; When the disease label indicates that the user does not need a follow-up visit, the first churn prediction result is used as a target churn prediction result.

8. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method for predicting user churn according to any one of claims 1 to 6 are realized.

9. A storage medium, which is a computer-readable storage medium and is used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method for predicting user churn according to any one of claims 1 to 6.

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