WO2018153029A1 - Data association-based doctor rating and recommendation system and method - Google Patents

Abstract

Provided are a data association-based doctor rating and recommendation system and method, said method comprising: obtaining medical data from a hospital information system (1) of each hospital (S10); analyzing the medical data of each hospital, and creating an illness type list according to an illness type keyword (S11); obtaining public review information from a registration review website (S12); searching in said registration review website according to a node keyword in the illness type list, and associating the retrieved review information with the node corresponding to the node keyword (S13); according to the retrieved review information, rating each doctor in the illness type list (S14); when a user queries a corresponding illness by means of a client (4), recommending the highest-rated doctor to the user and displaying on the client (4) of the user (S15). Big-data analysis is performed on the medical data so that it is convenient for a patient to find a doctor when suffering from a corresponding illness, saving time when the patient is searching.

Description

 Title: Invention Name: Doctor Rating Recommendation System and Method Based on Data Association

 [0001] The present invention relates to the field of medical big data, and in particular, to a doctor rating recommendation system and method based on data association.

 Background technique

 [0002] In recent years, with the rapid development of the Internet, cloud computing, and the Internet of Things, ubiquitous mobile devices, RF IDs, and wireless sensors generate data every minute, and hundreds of millions of users' Internet services are engraved. In the huge amount of interaction, the amount of data to be processed is huge, and the data is always growing at a rate of 50% per year. Business demand and competitive pressure put forward higher requirements for the practicality and effectiveness of data processing. The conventional technical means cannot be dealt with at all. Therefore, Big Data has become a recent technology hotspot and has attracted widespread attention.

 [0003] Big data technology can accelerate medical conjecture and discover the transformation of medical practice: With the growing private and public medical data, big data technology helps people store and manage medical big data and from large volume, high complexity The value of the data will be extracted, and related medical technologies and products will continue to emerge, which will likely open up a new golden generation for the medical industry.

 [0004] However, the current medical data analysis system analyzes and processes medical big data, and does not consider the factors that the user evaluates to the doctor, nor does it refer to the medical big data based on the doctor evaluation information in the registration system. For analysis and processing, doctors cannot be rated by big data, and patients often have to find the corresponding doctor through a large number of inquiries.

 technical problem

 [0005] A primary object of the present invention is to provide a doctor rating evaluation system and method based on data association, which aims to solve the technical problem of not analyzing and processing and recommending a doctor based on the evaluation system in the process of medical big data processing.

 Problem solution

 Technical solution

[0006] In order to achieve the above object, the present invention provides a doctor rating evaluation system based on data association, which operates In the data center, the data center is connected to the hospital information system, the client, and the registered website through a network, and the doctor-ranking recommendation system based on the data association includes:

 [0007] an obtaining module, configured to obtain medical data from a hospital information system of each hospital;

[0008] a creating module, configured to parse medical data of each hospital, and create a list of disease types according to a disease type keyword;

 [0009] an obtaining module, configured to obtain evaluation information from a registered website;

 [0010] an association module, configured to perform a search in the evaluation information according to a node keyword in a disease type list, and associate the retrieved evaluation information with a node corresponding to the node keyword;

[0011] a rating module, configured to score each doctor in the disease type list according to the retrieved evaluation information; and

 [0012] The display module is configured to: when the user queries the corresponding disease through the client, recommend the doctor with the highest score to the user, and display it on the client of the user.

[0013] Preferably, the medical data further includes a hospital name, a patient name, a patient's age, a diseased day, a disease cause, a disease diagnosis information, a drug name, a drug quantity, a doctor name, a doctor's office, a fee, and a patient's Contact information.

 [0014] Preferably, the disease type list is divided into three layers of nodes, the first layer node is a disease name node, the second layer node is a department node of the hospital where the disease type is located, and the third layer node is a doctor information node.

[0015] Preferably, the evaluation information includes evaluation content, praise or bad review.

 [0016] Preferably, the method for scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by using the following formula: Y=a*Xl-b*X2+c+d, wherein, Y score XI is the number of favorable reviews, X2 is the number of bad reviews, c is the default value corresponding to the hospital qualification, d is the default value corresponding to the doctor's title, and a and b are fixed parameters.

 [0017] In another aspect, the present invention further provides a doctor rating recommendation method based on data association, which is applied to a data center, and the data center is connected to a hospital information system, a client, and a registered website through a network, and the method includes:

 [0018] obtaining medical data from hospital information systems of various hospitals;

[0019] parsing the medical data of each hospital, and creating a disease type list according to the disease type keyword; [0020] obtaining the evaluation information from the registered website; [0021] performing a search in the evaluation information according to a node keyword in the disease type list, and associating the retrieved evaluation information with a node corresponding to the node keyword;

[0022] scoring each doctor in the disease type list according to the retrieved evaluation information; and

 [0023] When the user queries the corresponding disease through the client, the highest rated doctor is recommended to the user and displayed on the user's client.

 [0024] Preferably, the medical data further includes a hospital name, a patient name, a patient's age, a diseased day, a disease cause, a disease diagnosis information, a drug name, a drug quantity, a doctor name, a doctor's office, a fee, and a patient's Contact information.

 [0025] Preferably, the disease type list is divided into three layers of nodes, the first layer node is a disease name node, the second layer node is a department node of the hospital where the disease type is located, and the third layer node is a doctor information node.

[0026] Preferably, the step of associating the node information in the disease type list with the node information and associating the retrieved evaluation information with the node corresponding to the node keyword comprises the following steps: [0027] (1) Obtaining a node keyword in the disease type list, and searching for the corresponding evaluation information by using the keyword, wherein the node keyword in the disease type list may be a node name, or may be a certain one of the nodes Set the keyword;

[0028] (2) determining whether the node keyword in the disease type list has corresponding evaluation information;

[0029] (3) If an evaluation information includes a node keyword in the disease type list, the evaluation information is associated with a node corresponding to the node keyword.

[0030] Preferably, the method for scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by using the following formula: Y=a*Xl-b*X2+c+d, wherein, Y score , XI is the number of praise, X2 is the number of bad reviews, c is the default value corresponding to the hospital qualification, and d is the default value corresponding to the doctor's title.

, a and b are fixed parameters.

 Advantageous effects of the invention

 Beneficial effect

 [0031] The present invention adopts the above technical solution, and brings the technical effects as follows: According to the data association-based doctor rating recommendation system and method of the present invention, the medical data is analyzed by big data, thereby rating the doctor of the hospital, facilitating the patient. The doctor who picks up the disease chooses the doctor, which saves the patient's inquiry.

Brief description of the drawing DRAWINGS

 1 is a schematic diagram of an application environment of a doctor rating recommendation system based on data association according to the present invention;

2 is a schematic diagram of functional modules of a preferred embodiment of a physician association rating system based on data association of the present invention;

 3 is a flow chart of a preferred embodiment of a doctor-ranking recommendation method based on data association of the present invention;

4 is a schematic diagram of a list of disease types of the present invention.

 [0036] The implementation, functional features, and advantages of the present invention will be further described with reference to the accompanying drawings.

 BEST MODE FOR CARRYING OUT THE INVENTION

 BEST MODE FOR CARRYING OUT THE INVENTION

The specific embodiments, structures, features and utilities of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

 Referring to FIG. 1, FIG. 1 is a schematic diagram of an application environment of a doctor rating recommendation system based on data association according to the present invention. The data association based physician rating recommendation system 20 of the present invention operates in the data center 2. The data center 2 is communicatively coupled to one or more hospital information systems 1 (illustrated by three in FIG. 1) via the network 3 to acquire a plurality of medical data from the hospital information system 1. The medical data includes, but is not limited to, the name of the hospital, the name of the patient, the age of the patient, the disease, the cause of the disease, the diagnosis information of the disease, the name of the drug, the number of drugs, the name of the doctor, the department of the visit, the cost, and the contact information of the patient. (for example, email address, mobile number, and instant messaging account, etc.).

 [0039] The network 3 may be a wired communication network or a wireless communication network. The network 3 is preferably a wireless communication network including, but not limited to, a GSM network, a GPRS network, a CDMA network, a TD-SCDMA network, a WiMAX network, a TD-LTE network, an FDD-LTE network, and the like.

[0040] The data center 2 is communicably connected to one or more clients 4 (illustrated by taking three as an example in FIG. 1) through the network 3, and the doctor with the highest rating after the user is retrieved is recommended to the patient. In other embodiments, the data center 2 may further analyze and process the medical data, and send the analyzed disease association list (as shown in FIG. 4 to the associated list of diseases "fever") to the patient via the network 3. Corresponding client 4 . It should be noted that the client 4 is held by a user, and the user can obtain the client through the client 4. Medical data.

 [0041] The data center 2 is communicatively connected to the website 5 through the network 3, and is used to obtain evaluation information of the patient from the website 5 from the website. Specifically, the registered website 5 provides an API interface, and the device or system accessing the API interface can obtain evaluation information from the registered website 5. The data center 2 obtains the evaluation information of the doctor on the basis of the authorization of the registered website 5 (i.e., authorized access to the API interface provided by the registered website 5). The registered website 5 is connected to the one or more hospital information systems 1, and the patient can register through the registered website 5, and then the registration information is sent to the hospital information system 1 to form the registration information of the hospital. For example, the user selects the hospital A internal medicine registration number on the registered website 5, and after the website 5 generates the registration information, the hospital information system 1 sent to the hospital A forms the registration information of the hospital A. In addition, after the patient has seen the disease, the visiting doctor can be evaluated on the registered website 5, and the registered website 5 retains the rating information for other patients to view.

 [0042] It should be noted that the data center 2 is a server of a cloud platform or a data center, and can better manage and/or assist with the data transmission capability and data storage capability of the cloud platform or the data center. The data center 2 is connected to the client 4.

 [0043] The client 4 may be, but is not limited to, any other suitable portable electronic device such as a smart phone, a tablet computer, a personal digital assistant (PDA), a personal computer, an electronic signboard, and the like.

 [0044] Referring to FIG. 2, it is a schematic diagram of a functional module of a preferred embodiment of a physician-ranking recommendation system based on data association of the present invention. As shown in Fig. 1, in the present embodiment, the data association based doctor rating recommendation system 20 is applied to the data center 2. The data center 2 includes, but is not limited to, a doctor association rating system 20 based on data association, a storage unit 22, a processing unit 24, and a communication unit 26.

 [0045] The storage unit 22 may be a read only storage unit ROM, an electrically erasable storage unit EEPRO M, a flash storage unit FLASH or a solid hard disk.

 [0046] The processing unit 24 may be a central processing unit (CPU), a microcontroller (MCU), a data processing chip, or an information processing unit having a data processing function.

[0047] The communication unit 26 is a wireless communication interface with remote wireless communication functions, for example, supports communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE, FDD-LT E Communication interface. [0048] The data association-based doctor rating recommendation system 20 includes, but is not limited to, an acquisition module 200, a creation module 210, an association module 220, a rating module 230, and a display module 240, and the module referred to in the present invention refers to a module. A series of computer program instructions that can be executed by the processing unit 24 of the data center 2 and that are capable of performing a fixed function are stored in the storage unit 22 of the data center 2.

 [0049] The acquisition module 200 is configured to acquire medical data from the hospital information system 1 of each hospital.

 [0050] Specifically, the hospital information system 1 provides a data import interface (eg, an application program interface, API), and a device or system that accesses the data import interface can be from the hospital information system. Get medical data in 1. The obtaining module 200 invokes an API interface provided by the hospital information system 1 to obtain medical data.

 [0051] It should be noted that, since the medical data belongs to private information, in order to ensure information security, the medical data is sent to the data center 2, and the encryption and decryption algorithm is adopted (for example, the MD5 encryption and decryption algorithm and the RSA encryption and decryption algorithm). DES encryption and decryption algorithm, DSA encryption and decryption algorithm, AES encryption and decryption algorithm, etc.) The medical data is first encrypted and then transmitted to the data center 2.

 [0052] The creating module 210 is configured to parse the medical data of each hospital, and create a disease type list according to the disease type keyword. The disease type list is divided into three layers of nodes, the first layer node is a disease name node (the node holds disease name information), and the second layer node is a department node of the hospital where the disease type is located (the node holds the name of the hospital department), The third layer node is the doctor information node (this node saves the doctor's name, job title, receiving volume, favorable rate, etc.). As shown in Figure 4, the list of disease types is a list of disease "fever". In other embodiments, the list of disease types may be more than three layers (eg, four layers, five layers, or more)

 [0053] The obtaining module 200 is configured to obtain evaluation information from the website 5 for registration. The rating information may be, but is not limited to, rating content, praise or bad reviews, and the like.

 [0054] The association module 220 is configured to perform a search in the evaluation information according to a node keyword in the disease type list, and associate the retrieved evaluation information with a node corresponding to the node keyword.

 And the step of associating the retrieved evaluation information with the node corresponding to the node keyword according to the node keyword in the disease type list includes the following steps:

[0056] (1) acquiring a node keyword in the disease type list, and searching whether there is corresponding evaluation information by using the keyword, wherein the node keyword in the disease type list may be a node name, or may be A preset keyword in the node. For example, as shown in Figure 4, the housekeeping word of the hospital department node is "XX X People's Hospital Internal Medicine YY doctor";

 [0057] (2) determining whether the node keyword in the disease type list has corresponding evaluation information;

 [0058] (3) If an evaluation information includes a node keyword in the disease type list, the evaluation information is associated with a node corresponding to the node keyword.

 [0059] The rating module 230 is configured to score each doctor in the disease type list according to the retrieved evaluation information. In this embodiment, the method for scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by the following formula: Y=a*Xl-b*X2+c+d, wherein, Y score X 1 is the number of favorable reviews, X2 is the number of bad reviews, and c is the default value corresponding to hospital qualifications (for example, the default value corresponding to the top three hospitals is 50, the default value corresponding to the hospital is 45, and the default corresponding to the clinic hospital) The value is 4 0, etc.), d is the default value corresponding to the doctor's title (for example, the default value of the chief physician is 30, the default value of the deputy chief physician is 25, the default value of the physician is 20, etc.), a And b are fixed parameters (for example, a is a value of 15, b is a value of 10, etc.). It should be noted that the higher the value of Y calculated according to the formula, the higher the doctor's score.

 [0060] The display module 240 is configured to recommend the highest rated doctor to the patient when the user queries the corresponding disease through the client 4, and displays it on the client 4 of the user. Specifically, as shown in FIG. 4, if the doctor's score in the hospital of A hospital is up to 145 points, when the user queries the fever through the client 4, the doctor's information of the hospital A is displayed to the user. Client 4 on it.

 Referring to FIG. 3, there is shown a flow chart of a preferred embodiment of the doctor-ranking recommendation method based on data association of the present invention. In this embodiment, the data association based doctor rating recommendation method is applied to the data center 2, and the method includes the following steps:

 [0062] Step S10: The obtaining module 200 acquires medical data from the hospital information system 1 of each hospital.

 [0063] Specifically, the hospital information system 1 provides a data import interface (eg, an application program interface, an API), and a device or system that accesses the data import interface can be from the hospital information system. Get medical data in 1. The obtaining module 200 invokes an API interface provided by the hospital information system 1 to obtain medical data.

[0064] It should be noted that, since the medical data belongs to private information, in order to ensure information security, the medical data is sent to the data center 2, and the encryption and decryption algorithm is adopted (for example, the MD5 encryption and decryption algorithm, RSA) The encryption and decryption algorithm, the DES encryption and decryption algorithm, the DSA encryption and decryption algorithm, the AES encryption and decryption algorithm, etc.) first encrypt the medical data, and then transmit it to the data center 2.

[0065] Step S11: The creating module 210 parses the medical data of each hospital, and creates a disease type list according to the disease type keyword. The disease type list is divided into three layers of nodes, the first layer node is the disease name node (the node holds the disease name), the second layer is the department node of the hospital where the disease type is located (the node holds the disease name), and the third layer For the doctor information node (this node saves the doctor's name, title, etc.). As shown in Figure 4, the list of disease types is a list of diseases "fever". In other embodiments, the list of disease types may be more than three layers (e.g., four layers, five layers, or more).

[0066] Step S12: The obtaining module 200 acquires the evaluation information from the registered website 5. The rating information may be

, but not limited to, evaluation content, praise or bad reviews, and so on.

[0067] Step S13: The association module 220 searches the evaluation information according to the node keyword in the disease type list, and associates the retrieved evaluation information with the node corresponding to the node keyword.

And the step of associating the retrieved evaluation information with the node corresponding to the node keyword according to the node keyword in the disease type list includes the following steps:

[0069] (1) acquiring a node keyword in the disease type list, and searching whether there is corresponding evaluation information by using the keyword, wherein the node keyword in the disease type list may be a node name, or may be a node A default keyword. For example, as shown in Figure 4, the housekeeping word for the hospital department node is "XX"

X People's Hospital Internal Medicine";

 [0070] (2) determining whether the node keyword in the disease type list has corresponding evaluation information;

 (3) If a node keyword in the disease type list is included in one piece of evaluation information, the evaluation information is associated with a node corresponding to the node keyword.

[0072] Step S14: The rating module 230 scores each doctor in the disease type list according to the retrieved evaluation information. In this embodiment, the method for scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by the following formula: Y=a*Xl-b*X2+c+d, wherein, Y score XI is the number of favorable reviews, X2 is the number of bad reviews, and c is the default value corresponding to hospital qualifications (for example, the default value corresponding to the top three hospitals is 50, the default value corresponding to the dimethyl hospital is 45, and the default value corresponding to the clinic hospital For 40, etc.), d is the default value corresponding to the doctor's title (for example, the default value of the doctor's doctor is 30, the default value of the deputy doctor is 25, the default value of the doctor is 20, etc.), a and b Fixed reference Number (for example, a is a value of 15, b is a value of 10, etc.). It should be noted that the higher the value of Y calculated according to the formula, the higher the score of the doctor.

 [0073] Step S15: When the user queries the corresponding disease through the client 4, the display module 240 recommends the doctor with the highest score to the user and displays it on the client 4 of the patient. Specifically, as shown in Figure 4, if the doctor's score in the hospital of A hospital is up to 145 points, then when the patient queries the fever through the client 4, the doctor's information of the hospital A is displayed. Client 4 on it.

 The above are only the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and the equivalent structure or equivalent process transformations made by the description of the present invention and the contents of the drawings may be directly or indirectly applied to other related The technical field is equally included in the scope of patent protection of the present invention.

 Industrial applicability

 [0075] The present invention adopts the above technical solution, and brings the technical effects as follows: According to the data association-based doctor rating recommendation system and method of the present invention, big data analysis is performed on the medical data, thereby rating the doctor in the hospital, facilitating the patient. The doctor who picks up the disease chooses the doctor, which saves the patient's inquiry.

Claims

Claim A doctor-rating recommendation system based on data association, running in a data center, wherein the data center is connected to a hospital information system, a client, and a registered website through a network, and the doctor-ranking recommendation system based on data association includes: An acquisition module, configured to obtain medical data from a hospital information system of each hospital; a module for parsing medical data of each hospital, creating a disease type list according to a disease type keyword; and an obtaining module for obtaining an evaluation from the registered website An association module, configured to perform a search in the evaluation information according to a node keyword in a disease type list, and associate the retrieved evaluation information with a node corresponding to the node keyword; a rating module, configured to retrieve the The evaluation information is used to score each doctor in the disease type list; and a display module is configured to recommend the highest rated doctor to the user when the user queries the corresponding disease through the client, and displays it on the user's client. A data association based physician rating recommendation system according to claim 1 wherein The medical data also includes the name of the hospital, the name of the patient, the age of the patient, the time of illness, the cause of the disease, the diagnosis information of the disease, the name of the drug, the number of drugs, the name of the doctor, the clinic, the cost, and the contact information of the patient. A data association based physician rating recommendation system according to claim 1 wherein The disease type list is divided into three layers of nodes, the first layer node is a disease name node, the second layer node is a department node of the hospital where the disease type is located, and the third layer node is a doctor information node. A data association based physician rating recommendation system according to claim 1 wherein The evaluation information includes evaluation content, praise or bad review. A data association based physician rating recommendation system according to claim 1 wherein The method for scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by the following formula: Y=a*Xl-b*X2+c+d, wherein, Y score, XI is favorable Quantity, X2 is the number of bad reviews, c is the default value corresponding to the hospital qualification, d is the default value corresponding to the doctor's title, and a and b are fixed parameters. A doctor rating recommendation method based on data association, applied to a data center, characterized in The data center is connected to the hospital information system, the client, and the registered website through the network, and the method includes: obtaining medical data from the hospital information system of each hospital; parsing the medical data of each hospital, creating according to the disease type keyword a list of disease types; obtaining evaluation information from the registered website; searching in the evaluation information according to the node keyword in the disease type list, and associating the retrieved evaluation information with the node corresponding to the node keyword; according to the retrieved evaluation The information is scored for each doctor in the disease type list; and when the user queries the corresponding disease through the client, the highest rated doctor is recommended to the user and displayed on the user's client. The data association-based doctor rating recommendation method according to claim 6, wherein the medical data further includes a hospital name, a patient name, a patient's age, a diseased day, a disease cause, a disease diagnosis information, and a drug name. , the number of drugs, the name of the doctor, the department, the cost, and the contact details of the patient. The data association-based doctor rating recommendation method according to claim 6, wherein the disease type list is divided into three layers of nodes, the first layer node is a disease name node, and the second layer node is a hospital of the disease type. The department node, the third node is the doctor information node. The data association-based doctor rating recommendation method according to claim 6, wherein the evaluation information includes evaluation content, praise, or bad review. The data association-based doctor rating recommendation method according to claim 6, wherein the manner of scoring each doctor in the disease type list according to the retrieved evaluation information is calculated by the following formula: Y=a* Xl-b*X2+c+d, where Y score, XI is the number of favorable reviews, X2 is the number of bad reviews, c is the default value corresponding to the hospital qualification, d is the default value corresponding to the doctor's title, a and b are For fixed parameters.