WO2018153030A1 - Webpage-based doctor rating and recommendation system and method for medical informatization - Google Patents

Abstract

A webpage-based doctor rating and recommendation system and method for medical informatization, the method comprising: acquiring medical data from a hospital information system of each hospital (S10); parsing the medical data from each hospital, and creating a disease type list according to disease type keywords (S11); obtaining a medical news webpage from a search engine website (S12); carrying out retrieval in the medical news webpage according to a node keyword in the disease type list, and associating the retrieved medical news webpage with a node corresponding to the node keyword (S13); rating each doctor in the disease type list according to the retrieved medical news webpage (S14); and when a patient inquiries about a corresponding disease by means of a client, recommending the doctor having the highest score to the patient, and displaying the same on the client of the patient (S15). According to said method, medical data may be subjected to big data analysis, thereby aiding a patient in finding a corresponding doctor when the patient is sick and reducing the time it takes for the patient to inquire.

Description

 Medical information webpage-based doctor rating recommendation system and method

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

 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 the medical big data, and does not consider the factors of the user browsing the webpage, and does not analyze and process the medical big data based on the webpage browsing. Doctors cannot be rated by big data, and patients often have to find the corresponding doctor through a large number of queries.

 technical problem

 [0005] The main purpose of the present invention is to provide a medical information-based webpage-based doctor rating recommendation system and method, aiming at solving the existing technical problems in the process of medical big data processing without analyzing and processing based on web pages and recommending doctors. .

 Problem solution

 Technical solution

[0006] In order to achieve the above object, the present invention provides a medical informationization webpage-based doctor rating recommendation system. And running in the data center, the data center is connected to the hospital information system, the client, and the search engine website through a network, and the medical informationization webpage-based doctor rating recommendation system 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] the obtaining module is configured to obtain a medical news webpage from a search engine website;

 [0010] an association module, configured to search, in the medical news webpage, according to a node keyword in the disease type list, and associate the retrieved medical news webpage 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 medical news webpage; and

 [0012] a display module, configured to: when the patient queries the corresponding disease by the client, recommend the highest rated doctor to the patient, and display it on the patient's client.

[0013] Preferably, the medical data further includes a hospital name, a patient name, a patient's annual sputum, a diseased sputum, 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 and evaluation information for doctors.

[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 association module is specifically configured to:

 [0016] (1) acquiring a node keyword in the disease type list, and searching whether there is a corresponding medical news webpage by using the keyword, wherein the node keyword in the disease type list may be a node name or a node a preset keyword in the middle;

 [0017] (2) determining whether the node keyword in the disease type list has a corresponding medical news webpage;

 [0018] (3) If a medical news webpage includes a node keyword in the disease type list, the medical news webpage is associated with a node corresponding to the node keyword.

[0019] Preferably, the method for calculating each doctor in the disease type list according to the retrieved medical news webpage is calculated by using the following formula: Y=a*Xl-b*X2+c+d, wherein, Y Rating, XI is the number of positive medical news pages, X2 is the number of negative medical news pages, 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. [0020] In another aspect, the present invention further provides a medical informationization webpage-based doctor rating recommendation method, which is applied to a data center, and the data center is connected to a hospital information system, a client, and a search engine website through a network, the method include:

 [0021] obtaining medical data from hospital information systems of various hospitals;

 [0022] parsing medical data of each hospital, and creating a disease type list according to the disease type keyword; [0023] obtaining a medical news webpage from the search engine;

 [0024] performing a search in the medical news webpage according to a node keyword in the disease type list, and associating the retrieved medical news webpage with a node corresponding to the node keyword;

[0025] scoring each doctor in the disease type list according to the retrieved medical news webpage; and

[0026] When the patient queries the corresponding disease by the client, the highest rated doctor is recommended to the patient and displayed on the patient's client.

 [0027] Preferably, the medical data further includes a hospital name, a patient name, a patient's annual sputum, a diseased sputum, 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 and evaluation information for doctors.

[0028] 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.

[0029] Preferably, the searching is performed on the medical news webpage according to a node keyword in a disease type list.

And the step of associating the retrieved medical news webpage with the node corresponding to the node keyword includes the following steps:

 [0030] (1) acquiring a node keyword in the disease type list, and searching whether there is a corresponding medical news webpage by using the keyword, wherein the node keyword in the disease type list may be a node name or a node a preset keyword in the middle;

 [0031] (2) determining whether the node keyword in the disease type list has a corresponding medical news webpage;

 [0032] (3) If a medical news webpage includes a node keyword in the disease type list, the medical news webpage is associated with a node corresponding to the node keyword.

[0033] Preferably, the method for calculating each doctor in the disease type list according to the retrieved medical news webpage is calculated by using the following formula: Y=a*Xl-b*X2+c+d, wherein, Y Rating, XI is the number of positive medical news pages, X2 is the number of negative medical news pages, c is the corresponding for hospital qualifications The default value, d is the default value corresponding to the doctor's title, and a and b are fixed parameters.

 Advantageous effects of the invention

 Beneficial effect

 [0034] The present invention adopts the above technical solution, and brings the technical effects as follows: The medical informationization-based webpage-based doctor rating recommendation system and method, and the big data analysis of the medical data, thereby rating the doctor in the hospital, Facilitate the patient's illness, select the corresponding doctor, and save the patient's query.

 Brief description of the drawing

 DRAWINGS

 1 is a schematic diagram of an application environment of a medical information webpage-based doctor rating recommendation system according to the present invention; [0036] FIG. 2 is a functional block diagram of a preferred embodiment of the medical information webpage-based doctor rating recommendation system of the present invention;

 3 is a flow chart of a preferred embodiment of the medical information webpage-based doctor rating recommendation method of the present invention. [0038] FIG. 4 is a schematic diagram of a disease type list of the present invention.

 [0039] 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 functions 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.

[0041] Referring to FIG. 1, FIG. 1 is a schematic diagram of an application environment of a medical information webpage-based doctor rating recommendation system according to the present invention. The medical informationization web page based physician rating recommendation system 20 in 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, a hospital name, a patient's name, a patient's age, a diseased day, a disease cause, a disease diagnosis information, a drug name, a drug quantity, a doctor's name, a doctor's office, a fee, a patient's Contact information (for example, email address, mobile phone number, and instant messaging account number), and information about the doctor's evaluation.

 [0042] 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.

 [0043] The data center 2 is communicatively connected to one or more clients 4 (illustrated by three in FIG. 1) through the network 3, and the doctor with the highest rating after the patient search 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 patient, and the patient can obtain the medical data through the client 4.

 [0044] The data center 2 is communicatively coupled to the search engine website 5 via the network 3 for obtaining medical news web pages from the search engine website 5. Specifically, the search engine website 5 provides an API interface, and a device or system accessing the API interface can obtain a medical news webpage from the search engine website 5. The data center 2 obtains the medical news web page on the basis of the authorization of the search engine website 5 (i.e., authorized access to the API interface provided by the search engine website 5).

 [0045] 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.

 [0046] 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.

 [0047] Referring to FIG. 2, it is a functional block diagram of a preferred embodiment of the medical information-based web-based physician rating recommendation system of the present invention. As shown in Fig. 1, in the present embodiment, the medical informationization web page based doctor rating recommendation system 20 is applied to the data center 2. The data center 2 includes, but is not limited to, a medical information-based web-based physician rating recommendation system 20, a storage unit 22, a processing unit 24, and a communication unit 26.

[0048] The storage unit 22 may be a read only storage unit ROM, an electrically erasable storage unit EEPRO M, flash memory unit FLASH or solid hard disk.

 [0049] 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.

 [0050] The communication unit 26 is a wireless communication interface with remote wireless communication function, for example, supports communication technologies such as GSM, GPRS, WCDMA, CDMA, TD-SCDMA, WiMAX, TD-LTE, FDD-LT E Communication interface.

 [0051] The medical informationization webpage-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. The module referred to in the present invention is 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.

 [0052] The acquisition module 200 is configured to acquire medical data from the hospital information system 1 of each hospital.

 [0053] 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.

 [0054] 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.

 [0055] 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)

[0056] The obtaining module 200 is configured to obtain a medical news webpage from the search engine website 5. The medical news webpage may be, but is not limited to, a news webpage introduced by a medical disease, a negative medical news webpage (for example, Medical news pages for medical malpractices, news pages on the front of hospitals, and other types of medical news pages.

 [0057] The association module 220 is configured to perform a search on the medical news webpage according to a node keyword in the disease type list, and associate the retrieved medical news webpage with a node corresponding to the node keyword.

 And the step of associating the retrieved medical news webpage with the node corresponding to the node keyword according to the node keyword in the disease type list, and the following steps:

[0059] (1) acquiring a node keyword in the disease type list, and searching whether there is a corresponding medical news webpage by using the keyword, wherein the node keyword in the disease type list may be a node name or a node One of the default keywords. For example, as shown in Figure 4, the housekeeping word of the hospital department node is "XXX People's Hospital Internal Medicine";

 [0060] (2) determining whether the node keyword in the disease type list has a corresponding medical news webpage;

 [0061] (3) If a medical news webpage includes a node keyword in the disease type list, the medical news webpage is associated with a node corresponding to the node keyword.

 [0062] The rating module 230 is configured to score each doctor in the disease type list according to the retrieved medical news webpage. In this embodiment, the method for scoring each doctor in the disease type list according to the retrieved medical news webpage is calculated by the following formula: Y=a*Xl-b*X2+c+d, where Y Rating, XI is the number of positive medical news pages, X2 is the number of negative medical news pages, c is the default value corresponding to the hospital qualification (for example, the default value of the top three hospitals is 50, the default value corresponding to the dimethyl hospital) 45. The default value of the clinic hospital is 40, etc.), d is the default value corresponding to the doctor's title (for example, the default value of the chief doctor is 30, the default value of the deputy doctor is 25, and the default value of the doctor 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.

[0063] The display module 240 is configured to recommend the highest rated doctor to the patient when the patient queries the corresponding disease defect through the client 4, and displays it on the patient's client 4. 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. In addition, when multiple doctors score the same, the display module 240 selects a doctor who has received more than the preset value (10000 times) and the favorable rate is greater than the preset rate (95%). [0064] Referring to FIG. 3, it is a flowchart of a preferred embodiment of the medical informationization webpage-based doctor rating recommendation method of the present invention. In this embodiment, the medical informationization webpage-based doctor rating recommendation method is applied to the data center 2, and the method includes the following steps:

 [0065] Step S10: The obtaining module 200 acquires medical data from the hospital information system 1 of each hospital.

 [0066] 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.

 [0067] 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.

 [0068] 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 nodes, the first node is the disease name node (the node holds the disease name), and the second layer is the department node of the hospital where the disease type is located.

(The node saves the name of the disease), and the third layer is the doctor information node (this node saves the doctor's name, job title, number of consultations, favorable rate, 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).

 [0069] Step S12: The obtaining module 200 acquires a medical news webpage from the search engine website 5. The medical news webpage may be, but is not limited to, a news webpage for medical disease introduction, a negative medical news webpage (for example, a medical news webpage for medical malpractice), a news webpage of a hospital front, and other types of medical news webpages.

 [0070] Step S13: The association module 220 searches the medical news web page according to the node keyword in the disease type list, and associates the retrieved medical news webpage with the node corresponding to the node keyword.

[0071] The step of associating the retrieved medical news webpage with the node corresponding to the node keyword according to the node keyword in the disease type list is performed in the medical news webpage, and the following steps are as follows: [0072] (1) acquiring a node keyword in the disease type list, and searching whether there is a corresponding medical news webpage by using the keyword, wherein the node keyword in the disease type list may be a node name or a node One of the default keywords. For example, as shown in Figure 4, the housekeeping word of the hospital department node is "XXX People's Hospital Internal Medicine";

 [0073] (2) determining whether the node keyword in the disease type list has a corresponding medical news webpage;

 [0074] (3) If a medical news webpage includes a node keyword in the disease type list, the medical news webpage is associated with a node corresponding to the node keyword.

[0075] Step S14: The rating module 230 scores each doctor in the disease type list according to the retrieved medical news webpage. In this embodiment, the method for scoring each doctor in the disease type list according to the retrieved medical news webpage is calculated by the following formula: Y _{= a} * _X1 _ _b * _{X2 + c + d} , where, Y Rating, XI is the number of positive medical news pages, X2 is the number of negative medical news pages, c is the default value corresponding to the hospital qualification (for example, the default value of the top three hospitals is 50, the default value corresponding to the dimethyl hospital) 45. The default value of the clinic hospital is 40, etc.), d is the default value corresponding to the doctor's title (for example, the default value of the chief doctor is 30, the default value of the deputy doctor is 25, and the default value of the doctor 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 score of the doctor.

[0076] Step S15: When the patient queries the corresponding disease by the client 4, the display module 240 recommends the doctor with the highest score to the patient 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. In addition, when multiple doctors score the same, the display module 240 selects a doctor who is more than the preset value (10000 times) and the favorable rate is greater than the preset rate ( ₉₅ <3⁄4).

 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

[0078] The present invention adopts the above technical solution, and brings the technical effects as follows: The medical informationization based network of the present invention The doctor's rating recommendation system and method of the page, the big data analysis of the medical data, thereby rating the doctors in the hospital, facilitating the patient to pick up the corresponding doctor, and saving the time of the patient's inquiry.

Claims

Claim A medical informationization webpage-based doctor rating recommendation system running in a data center, wherein the data center is connected to a hospital information system, a client, and a search engine website through a network, and the medical informationization is based on a webpage doctor The rating recommendation system includes: an obtaining module, configured to obtain medical data from a hospital information system of each hospital; a creating module, configured to parse medical data of each hospital, and create a disease type list according to a disease type keyword; For obtaining a medical news webpage from a search engine website; an association module, configured to perform a search in the medical news webpage according to a node keyword in a disease type list, and perform a node corresponding to the node keyword corresponding to the retrieved medical news webpage Correlation; a rating module, configured to score each doctor in the disease type list according to the retrieved medical news webpage; and a display module, configured to recommend the highest rated doctor to the patient when the patient queries the corresponding disease through the client And shown in the suffering The client. The medical information webpage-based doctor rating recommendation system according to claim 1, wherein the medical data further includes a hospital name, a patient name, a patient's age, a diseased condition, a disease cause, a disease diagnosis information, The name of the drug, the number of drugs, the name of the doctor, the department of the visit, the cost, the contact information of the patient, and the evaluation information of the doctor. The medical information webpage-based doctor 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, and the second layer node is the disease type. At the department node of the hospital, the third node is the doctor information node. The medical information webpage-based doctor rating recommendation system according to claim 3, wherein the association module is specifically configured to: (1) acquire a node keyword in a disease type list, and search by the keyword Whether there is a corresponding medical news webpage; (2) determining whether the node keyword in the disease type list has a corresponding medical news webpage; (3) if a medical news webpage includes a node keyword in the disease type list, Then, the medical news webpage is associated with a node corresponding to the node keyword. The medical information webpage-based doctor rating recommendation system according to claim 1, wherein said doctor according to the retrieved medical news webpage lists each doctor in the disease type list The way to score is calculated using the following formula: Y=a*Xl-b*X2+c+d, where Y score, XI is the number of positive medical news pages, and X2 is the number of negative medical news pages, c For 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.  [Claim 6] A medical informationization-based webpage-based doctor rating recommendation method is applied to a data center, wherein the data center is connected to a hospital information system, a client, and a search engine website through a network, and the method includes: Obtaining medical data from the hospital information system of each hospital; parsing the medical data of each hospital, creating a disease type list according to the disease type keyword; obtaining a medical news webpage from the search engine website; according to the node keyword in the disease type list Searching in the medical news webpage, and associating the retrieved medical news webpage with the node corresponding to the node keyword; scoring each doctor in the disease type list according to the retrieved medical news webpage; and when the patient passes the client check Ask for the corresponding disease, recommend the highest rated doctor to the patient, and display it on the patient's client.  [Claim 7] The medical informationization webpage-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, and a disease cause. , disease diagnosis information, drug name, number of drugs, doctor's name, visiting department, cost, patient contact information, and evaluation information for doctors.  [Claim 8] The medical informationization-based webpage-based doctor rating recommendation method according to claim 6, wherein the disease type list is divided into three layers of nodes, and the first layer node is a disease name node, and the second layer The node is the department node of the hospital where the disease type is located, and the third node is the doctor information node. [Claim 9] The medical informationization-based webpage-based doctor rating recommendation method according to claim 8, wherein the searching is performed on the medical news webpage according to a node keyword in a disease type list, and the retrieval is performed The step of associating the medical news webpage with the node corresponding to the node keyword includes the following steps: (1) acquiring a node keyword in the disease type list, and searching whether there is a corresponding medical news webpage by using the keyword; Determining whether the node keyword in the disease type list has a corresponding medical news webpage; (3) if a medical news webpage includes a node keyword in the disease type list, The medical news web page is associated with a node corresponding to the node keyword.  [Claim 10] The medical informationization-based webpage-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 medical news webpage is as follows The formula is calculated: Y=a*Xl-b*X2+c+d, where Y score, XI is the number of positive medical news pages, X2 is the number of negative medical news pages, c is the default corresponding to hospital qualifications Value, d is the default value corresponding to the doctor's title, and a and b are fixed parameters.