Method and device for obtaining disease diagnosis related groups and medium

Abstract

The application discloses a disease diagnosis related grouping acquisition method, which comprises the following steps: acquiring a query keyword of a disease diagnosis related grouping corresponding to target disease diagnosis data of a target patient according to the target disease diagnosis data; acquiring a disease diagnosis related grouping set corresponding to the target disease diagnosis data according to the query keyword; and acquiring a target disease diagnosis related grouping corresponding to the target patient from the disease diagnosis related grouping set. The disease diagnosis related grouping acquisition method, device and medium disclosed by the application can acquire a DRG grouping when diagnosis is performed, and can effectively reduce the waste of medical resources.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus and medium for obtaining disease diagnosis-related groups. Background Technology

[0002] Currently, medical insurance typically charges based on Diagnosis Related Groups (DRGs). Different groups are assigned different weights, and the payment amount is determined according to the weights. DRG groups are determined based on the diagnostic data and diagnostic procedures in the post-discharge medical record. Using DRG group payment can standardize medical behavior and reduce overtreatment.

[0003] However, existing DRG grouping is usually done after discharge, and clinicians allocate medical resources based on their own experience, resulting in a low matching degree of DRG grouping for allocating medical resources and causing waste of medical resources. Therefore, there is an urgent need for a method that can obtain DRG grouping in advance. Summary of the Invention

[0004] This invention provides a method, apparatus, and medium for obtaining disease diagnosis-related groups (DRGs), which can obtain DRGs during diagnosis and effectively reduce the waste of medical resources.

[0005] A first aspect of this invention provides a method for obtaining disease diagnosis-related groups, the method comprising:

[0006] Based on the target patient's target disease diagnosis data, obtain the query keywords for the disease diagnosis-related groups corresponding to the target disease diagnosis data;

[0007] Based on the query keywords, obtain the disease diagnosis-related group set corresponding to the target disease diagnosis data;

[0008] Obtain the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set.

[0009] Optionally, obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set includes:

[0010] Select disease diagnosis-related groups from the set of disease diagnosis-related groups that match the target disease diagnosis data, and use the selected disease diagnosis-related groups as the target disease diagnosis-related groups.

[0011] Optionally, after obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, the method further includes:

[0012] Based on the target disease diagnosis data, obtain the relevant symptom data of the target patient, wherein the relevant symptom data includes at least one of complication data and comorbidity data;

[0013] Determine whether the relevant symptom data conforms to the grouping rules for the diagnosis-related grouping of the target disease;

[0014] If the criteria are met, the relevant symptom data will be added to the target disease diagnosis-related group; if the criteria are not met, the relevant symptom data will be prohibited from being added to the target disease diagnosis-related group.

[0015] Optionally, determining whether the relevant symptom data conforms to the grouping rules of the target disease diagnosis-related grouping includes:

[0016] Obtain the degree of correlation between the relevant symptom data and the diagnostic correlation group of the target disease;

[0017] Based on the degree of relationship, determine whether the related symptom data conforms to the grouping rules.

[0018] Optionally, after obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, the method includes:

[0019] Obtain the data of the items to be examined from the target patient;

[0020] Add the data of the item to be checked to the target disease diagnosis-related group.

[0021] Optionally, obtaining the target patient's data for the items to be examined includes:

[0022] Based on the target disease diagnosis data, obtain the set of test items corresponding to the target patient;

[0023] Based on the selection operation of multiple detection items in the set of items to be detected, multiple detection items corresponding to the selection are obtained as the data of the items to be checked.

[0024] Optionally, after obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, the method further includes:

[0025] Obtain the target patient's in-department testing data in the target clinical department;

[0026] Add the data to be tested within the department to the target disease diagnosis-related group.

[0027] Optionally, after adding the intra-departmental data to be tested to the target disease diagnosis-related group, the method further includes:

[0028] Based on the data of the items to be investigated and the data to be tested within the department, obtain the clinical pathway data of the target patient.

[0029] A second aspect of the present invention also provides a device for obtaining disease diagnosis-related groups, the device comprising:

[0030] The keyword acquisition unit is used to acquire query keywords for disease diagnosis-related groups corresponding to the target disease diagnosis data based on the target patient's target disease diagnosis data.

[0031] The disease diagnosis group set acquisition unit is used to acquire the disease diagnosis-related group set corresponding to the target disease diagnosis data based on the query keywords.

[0032] The target disease diagnosis group acquisition unit is used to acquire the target disease diagnosis related group corresponding to the target patient from the disease diagnosis related group set.

[0033] A third aspect of the present invention provides an electronic device including a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs containing operation instructions for performing the method for obtaining disease diagnosis-related groups as provided in the first aspect.

[0034] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps corresponding to the method for obtaining disease diagnosis-related groups provided in the first aspect.

[0035] The above-described one or more technical solutions in the embodiments of this application have at least the following technical effects:

[0036] Based on the above technical solution, query keywords for disease diagnosis-related groups corresponding to the target disease diagnosis data are obtained according to the target patient's target disease diagnosis data; a set of disease diagnosis-related groups corresponding to the target disease diagnosis data is obtained according to the query keywords; and the target disease diagnosis-related groups corresponding to the target patient are obtained from the set of disease diagnosis-related groups. Since the set of disease diagnosis-related groups is obtained from the query keywords derived from the target patient's target disease diagnosis data, the DRG groups in the set of disease diagnosis-related groups have a high degree of matching with the target patient. Furthermore, obtaining the target disease diagnosis-related groups for the target patient from the set of disease diagnosis-related groups also results in a high degree of matching between the obtained target disease diagnosis-related groups and the target patient. Thus, obtaining the target disease diagnosis-related groups in advance before treatment and ensuring high accuracy of the obtained target disease diagnosis-related groups allows for more rational allocation of medical resources, thereby effectively reducing the waste of medical resources. Attached Figure Description

[0037] Figure 1 A flowchart illustrating the method for obtaining disease diagnosis-related groups provided in an embodiment of this application;

[0038] Figure 2 A block diagram of a device for obtaining disease diagnosis-related groups provided in an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0040] The main implementation principles, specific implementation methods, and corresponding beneficial effects of the technical solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0041] Example

[0042] Please refer to Figure 1 This application provides a method for obtaining disease diagnosis-related groups, the method comprising:

[0043] S101. Based on the target patient's target disease diagnosis data, obtain the query keywords for the disease diagnosis-related groups corresponding to the target disease diagnosis data;

[0044] S102. Based on the query keywords, obtain the disease diagnosis-related group set corresponding to the target disease diagnosis data;

[0045] S103. Obtain the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set.

[0046] The method for obtaining disease diagnosis-related groups in the embodiments of this specification is typically applied in a server or user terminal. The server may be, for example, a desktop computer, a laptop computer, an all-in-one computer, or a tablet computer, and the user terminal may be, for example, a desktop computer, a laptop computer, an all-in-one computer, a tablet computer, or a smartphone.

[0047] In step S101, the target patient's target disease diagnosis data can be read from the hospital database, or the target patient's target disease diagnosis data can be obtained based on the input target patient's target disease diagnosis data. After obtaining the target disease diagnosis data, keyword extraction is performed on the target disease diagnosis data to extract the corresponding query keywords.

[0048] In one example, when extracting keywords from the target disease diagnosis data, one can extract the target patient's master diagnosis data from the target disease diagnosis data and extract keywords from the master diagnosis data as query keywords; alternatively, one can pre-establish a keyword library and extract at least one keyword that matches the keyword library from the target disease diagnosis data as query keywords.

[0049] For example, taking target patient B as an example, the target disease diagnosis data of B is extracted from the hospital database. The target disease diagnosis data includes routine physical examination data, primary diagnosis data and related diagnosis data, etc. The related diagnosis data includes complication data and comorbidity data, etc. The primary diagnosis data is extracted from the target disease diagnosis data. If the primary diagnosis data includes diabetes, then diabetes in the primary diagnosis data is extracted as the query keyword.

[0050] After obtaining the query keywords, proceed to step S102.

[0051] In step S102, after obtaining the query keywords in step S101, the query keywords are used as search keywords to perform a query in the pre-set DRG group database, and at least one DRG group is retrieved and used as the DRG group set.

[0052] In one embodiment, the DRG grouping database can be created based on existing medical insurance DRG groups, thereby improving the accuracy of DRG groups in the database.

[0053] In one embodiment, after obtaining the query keywords, the query keywords can be entered into the search box on the group query page to perform the query, obtain the DRG group set, and display some or all of the DRG groups in the DRG group set on the current page, so that the groups in the DRG group set can be viewed more intuitively.

[0054] Specifically, when displaying some or all of the DRG groups in a DRG group set on the current page, the DRG groups to be displayed in the DRG group set can be determined based on the display area of ​​the current page. If the number of DRG groups in the DRG group set is small, all DRG groups in the DRG group set can be displayed in the display area. If the number of DRG groups in the DRG group set is large, causing a mismatch between the display area and the number of DRG groups, some DRG groups in the DRG group set can be displayed in the display area.

[0055] After obtaining the DRG group set, proceed to step S103.

[0056] In step S103, after obtaining the DRG group set, disease diagnosis-related groups that match the target disease diagnosis data are selected from the DRG group set, and the selected disease diagnosis-related groups are used as the target disease diagnosis-related groups.

[0057] In one embodiment, a selection operation for a specific DRG group in the DRG group set can be obtained, and the DRG group corresponding to the selection operation can be used as the target DRG group.

[0058] In one embodiment, when selecting DRG groups, the matching degree between the target disease diagnosis data and each DRG group in the DRG group set can be obtained; the DRG group with the highest matching degree is taken as the facial DRG group. To obtain the matching degree between the target disease diagnosis data and each DRG group, the historical disease diagnosis data corresponding to each DRG group and the target disease diagnosis data can be compared for similarity, and the obtained similarity result is taken as the matching degree between the target disease diagnosis data and each DRG group.

[0059] For example, if the DRG group set contains DRG groups A1, A2, A3 and A4, the similarity comparison between the target disease diagnosis data and the historical disease data of A1, A2, A3 and A4 is performed respectively. The matching degree between the target disease diagnosis data and A1, A2, A3 and A4 is 55%, 65%, 92% and 44% respectively. Since the maximum matching degree is 92%, A3, which corresponds to 92%, is taken as the target DRG group.

[0060] Because the target DRG group is obtained by comparing the similarity between the target disease diagnosis data and the historical disease diagnosis data corresponding to each DRG group, and the DRG group with the highest matching degree is used as the target DRG group, the matching degree between the obtained target DRG group and the target disease diagnosis data is higher, thereby improving the accuracy of the obtained target DRG group. Thus, the technical solution adopted in this application embodiment can obtain the target disease diagnosis-related group in advance before treatment and ensure that the accuracy of the obtained target disease diagnosis-related group is high. After obtaining the target DRG group in advance, medical resources can be rationally allocated according to the target DRG group, thereby effectively reducing the waste of medical resources.

[0061] In another embodiment, after obtaining the target DRG group, the average length of stay, weight, and payment standard corresponding to the target DRG group can also be obtained and displayed, so that the payment standard corresponding to the target DRG group can be used as a reference for the cost of inpatient examination and treatment upon admission.

[0062] In another embodiment, after obtaining the target DRG group corresponding to the target patient from the DRG group set, the relevant symptom data of the target patient can also be obtained based on the target disease diagnosis data, wherein the relevant symptom data includes at least one of comorbidity data and comorbidity data; it is determined whether the relevant symptom data conforms to the grouping rules of the target DRG group; if it does, the relevant symptom data is added to the target DRG group; if it does not, the addition of the relevant symptom data to the target DRG group is prohibited.

[0063] In one embodiment, the relevant symptom data can be comorbidity data or comorbidity data. Of course, the relevant symptom data can include comorbidity data and comorbidity data, etc., and this specification does not impose specific limitations.

[0064] In one embodiment, when acquiring related symptom data, the related disease diagnosis data in the target disease diagnosis data can be analyzed to obtain related symptom data. The related symptom data may include, for example, lower extremity deep vein thrombosis, lower extremity phlebitis, lower extremity intramuscular thrombosis, lower extremity venous occlusion, varicose veins of the lower extremities during pregnancy, and varicose veins of the lower extremities with ulcers.

[0065] Specifically, when determining whether the relevant symptom data conforms to the grouping rules of the target DRG group, the closeness of the relationship between the relevant symptom data and the target DRG group can be obtained; based on the closeness of the relationship, it can be determined whether the relevant symptom data conforms to the grouping rules.

[0066] In one embodiment, when obtaining the degree of relationship between relevant symptom data and target DRG group, it can be determined whether there is a conflict between the relevant symptom data and the primary diagnosis corresponding to the target DRG group. If there is a conflict, the degree of relationship is determined to indicate that the relevant symptom data is closely related to the target DRG group, and the relevant symptom data is prohibited from being added to the DRG group. If there is no conflict, the degree of relationship is determined to indicate that the relevant symptom data is not closely related to the target DRG group, and the relevant symptom data is added to the DRG group.

[0067] Specifically, closely related groups and loosely related groups can be set in advance for the target DRG group. If the related symptom data conflicts with the primary diagnosis corresponding to the target DRG group, the related symptom data is added to the closely related group and the addition of related symptom data to the DRG group is prohibited. If the related symptom data does not conflict with the primary diagnosis corresponding to the target DRG group, the related symptom data is added to the loosely related group and the related symptom data is added to the DRG group.

[0068] In practical application, taking user B as an example, after obtaining B's target disease diagnosis data, if the primary diagnosis data in the target disease diagnosis data is old cerebral infarction, then old cerebral infarction is used as the query keyword. The DRG group selected is group C1, which includes other neurological diseases with serious complications or comorbidities, as the target DRG group. After obtaining C1, other disease diagnosis data besides the primary disease diagnosis data can be obtained from the target disease diagnosis data. The related symptom data is lower extremity venous thrombosis. The closeness of the relationship between lower extremity venous thrombosis and C1 is obtained. The search is conducted in the closely related and not closely related groups corresponding to C1. If lower extremity venous thrombosis is found in the closely related group corresponding to C1, then the related symptom data is prohibited from being added to the DRG group; if lower extremity venous thrombosis is found in the not closely related group corresponding to C1, then the related symptom data is added to the DRG group.

[0069] In this way, after obtaining the target DRG group, it is possible to determine whether the relevant symptom data of the target patient can be added to the target DRG group, thus making the obtained target DRG group more accurate.

[0070] In another embodiment, after obtaining the target DRG group corresponding to the target patient from the DRG group set, the patient's examination data can also be obtained; the examination data is then added to the target DRG group.

[0071] Specifically, when acquiring the data of the items to be tested for the target patient, the set of items to be tested for the target patient can be obtained based on the diagnostic data of the target disease; and based on the selection operation of multiple test items in the acquired set of test items, multiple test items corresponding to the selected items can be acquired and selected as the data of the items to be tested.

[0072] In one embodiment, when acquiring the data of items to be examined for a target patient, the historical examination item set corresponding to the target DRG group can be used as the test item set corresponding to the target patient based on the target disease diagnosis data. The test item set includes routine examination item set and special test item set, etc. Then, the selection operation for multiple test items in the test item set is obtained, and the selected multiple test items are used as the data of items to be examined.

[0073] Specifically, it can obtain the routine selection operation for routine inspection items in the routine inspection item set, obtain the selected routine items corresponding to the routine selection operation, and obtain the special selection operation for special inspection items in the special inspection item set, obtain the selected special items corresponding to the special selection operation, and use the selected routine items and selected special items as the data of the items to be inspected.

[0074] In this way, after obtaining the target DRG group, the data of the items to be examined for the target patient can also be obtained. The data of the items to be examined is selected from the set of historical examination items corresponding to the target DRG group, which makes the matching degree of the target patient of the data of the items to be examined higher.

[0075] In another implementation, after obtaining the target DRG group corresponding to the target patient from the DRG group set, the intra-departmental test data of the target patient in the target clinical department can also be obtained; the intra-departmental test data is added to the target DRG group.

[0076] Specifically, after obtaining the target DRG group, the target clinical department corresponding to the target patient can be obtained, the set of tests within the target clinical department can be obtained, and the tests to be tested can be selected from the set of tests within the department as the tests to be tested within the department, based on the target disease diagnosis data, and the tests to be tested within the department can be added to the target DRG group.

[0077] Specifically, since each clinical department has its own separate testing items, after obtaining the target clinical department, it is also necessary to obtain the data to be tested from the department's testing items and add it to the target DRG group to make the data in the target DRG group richer and more accurate.

[0078] In another embodiment, after adding the data of the items to be checked and the data to be tested within the department to the target DRG group, the clinical pathway data of the target patient can also be obtained based on the data of the items to be checked and the data to be tested within the department.

[0079] Specifically, after adding the data of the items to be tested and the data to be tested within the department to the target DRG group, the total payment cost for the target patient can be obtained based on the average length of stay, weight, and payment standard corresponding to the target DRG group, the payment amount for the tests corresponding to the data of the items to be tested, and the treatment amount within the department corresponding to the data to be tested within the department. This allows the prepaid payment cost to be set according to the total payment cost for the target patient, thereby reducing the problem of excessive deviation between the prepaid payment cost and the total payment cost.

[0080] The above-described one or more technical solutions in the embodiments of this application have at least the following technical effects:

[0081] Based on the above technical solution, query keywords for disease diagnosis-related groups corresponding to the target disease diagnosis data are obtained according to the target patient's target disease diagnosis data; a set of disease diagnosis-related groups corresponding to the target disease diagnosis data is obtained according to the query keywords; and the target disease diagnosis-related groups corresponding to the target patient are obtained from the set of disease diagnosis-related groups. Since the set of disease diagnosis-related groups is obtained from the query keywords derived from the target patient's target disease diagnosis data, the DRG groups in the set of disease diagnosis-related groups have a high degree of matching with the target patient. Furthermore, obtaining the target disease diagnosis-related groups for the target patient from the set of disease diagnosis-related groups also results in a high degree of matching between the obtained target disease diagnosis-related groups and the target patient. Thus, obtaining the target disease diagnosis-related groups in advance before treatment and ensuring high accuracy of the obtained target disease diagnosis-related groups allows for more rational allocation of medical resources, thereby effectively reducing the waste of medical resources.

[0082] In line with the above embodiments that provide a method for obtaining disease diagnosis-related groups, this application also provides a corresponding device for obtaining disease diagnosis-related groups. Please refer to... Figure 2 The device includes:

[0083] The keyword acquisition unit 201 is used to acquire query keywords for disease diagnosis-related groups corresponding to the target disease diagnosis data based on the target patient's target disease diagnosis data.

[0084] The disease diagnosis group set acquisition unit 202 is used to acquire the disease diagnosis-related group set corresponding to the target disease diagnosis data based on the query keywords.

[0085] The target disease diagnosis group acquisition unit 203 is used to acquire the target disease diagnosis related group corresponding to the target patient from the disease diagnosis related group set.

[0086] In one optional embodiment, the target disease diagnosis group acquisition unit 203 is used to select disease diagnosis related groups that match the target disease diagnosis data from the set of disease diagnosis related groups, and use the selected disease diagnosis related groups as the target disease diagnosis related groups.

[0087] In one alternative implementation, it further includes:

[0088] The related symptom data acquisition unit is used to acquire related symptom data of the target patient based on the target disease diagnosis data after acquiring the target disease diagnosis related group corresponding to the target patient from the disease diagnosis related group set, wherein the related symptom data includes at least one of complication data and comorbidity data;

[0089] The judgment unit is used to determine whether the relevant symptom data conforms to the grouping rules of the target disease diagnosis-related grouping;

[0090] A control unit is configured to, if the condition is met, add the relevant symptom data to the target disease diagnosis-related group; if the condition is not met, prevent the relevant symptom data from being added to the target disease diagnosis-related group.

[0091] In one optional implementation, the judgment unit is used to obtain the degree of correlation between the related symptom data and the target disease diagnosis-related grouping; and to determine whether the related symptom data conforms to the grouping rules based on the degree of correlation.

[0092] In one alternative implementation, it further includes:

[0093] The item to be checked acquisition unit is used to acquire the item to be checked data of the target patient after acquiring the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set; and to add the item to be checked data to the target disease diagnosis-related group.

[0094] In one optional implementation, the item to be checked acquisition unit is used to acquire a set of items to be tested corresponding to the target patient based on the target disease diagnosis data; and to acquire multiple test items corresponding to the selection as the item to be checked data based on the acquired selection operation for multiple test items in the set of items to be tested.

[0095] In one alternative implementation, it further includes:

[0096] The departmental project acquisition unit is used to obtain the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, then obtain the departmental test data of the target patient in the target clinical department; and add the departmental test data to the target disease diagnosis-related group.

[0097] In one alternative implementation, it further includes:

[0098] The clinical pathway acquisition unit is used to acquire the clinical pathway data of the target patient based on the data of the items to be examined and the data of the items to be examined within the department after adding the data to be tested within the department to the target disease diagnosis-related group.

[0099] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0100] Figure 3 This is a block diagram illustrating an electronic device 800 for a method of obtaining disease diagnosis-related groups according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0101] Reference Figure 3 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / display (I / O) interface 812, a sensor component 814, and a communication component 816.

[0102] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0103] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0104] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0105] Multimedia component 808 includes a screen that provides a display interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0106] Audio component 810 is configured to display and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for displaying audio signals.

[0107] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0108] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0109] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0110] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0111] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0112] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0113] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for obtaining disease diagnosis-related groups, characterized in that, The method includes: Based on the target patient's target disease diagnosis data, obtain the query keywords for the disease diagnosis-related groups corresponding to the target disease diagnosis data; Based on the query keywords, obtain the disease diagnosis-related group set corresponding to the target disease diagnosis data; Obtain the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set; Based on the target disease diagnosis data, obtain the relevant symptom data of the target patient, wherein the relevant symptom data includes at least one of complication data and comorbidity data; Determine whether the relevant symptom data conflicts with the primary diagnosis corresponding to the target disease diagnosis-related group; If the relevant symptom data exists, adding it to the target disease diagnosis-related group is prohibited; if it does not exist, the relevant symptom data is added to the target disease diagnosis-related group.

2. The acquisition method as described in claim 1, characterized in that, The step of obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set includes: Select disease diagnosis-related groups from the set of disease diagnosis-related groups that match the target disease diagnosis data, and use the selected disease diagnosis-related groups as the target disease diagnosis-related groups.

3. The acquisition method as described in claim 2, characterized in that, After obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, the method includes: Obtain the data of the items to be examined from the target patient; Add the data of the item to be checked to the target disease diagnosis-related group.

4. The acquisition method as described in claim 3, characterized in that, The acquisition of the target patient's examination data includes: Based on the target disease diagnosis data, obtain the set of test items corresponding to the target patient; Based on the selection operation of multiple detection items in the set of items to be detected, multiple detection items corresponding to the selection are obtained as the data of the items to be checked.

5. The acquisition method as described in claim 4, characterized in that, After obtaining the target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set, the method further includes: Obtain the target patient's in-department testing data in the target clinical department; Add the data to be tested within the department to the target disease diagnosis-related group.

6. The acquisition method as described in claim 5, characterized in that, After adding the intra-departmental data to be tested to the target disease diagnosis-related group, the method further includes: Based on the data of the items to be investigated and the data to be tested within the department, obtain the clinical pathway data of the target patient.

7. A device for acquiring disease diagnosis-related groups, characterized in that, The device includes: The keyword acquisition unit is used to acquire query keywords for disease diagnosis-related groups corresponding to the target disease diagnosis data based on the target patient's target disease diagnosis data. The disease diagnosis group set acquisition unit is used to acquire the disease diagnosis-related group set corresponding to the target disease diagnosis data based on the query keywords. A target disease diagnosis group acquisition unit is used to acquire a target disease diagnosis-related group corresponding to the target patient from the disease diagnosis-related group set. The related symptom data acquisition unit is used to acquire related symptom data of the target patient based on the target disease diagnosis data, wherein the related symptom data includes at least one of complication data and comorbidity data; The judgment unit is used to determine whether the related symptom data conflicts with the primary diagnosis corresponding to the target disease diagnosis-related group; A control unit is configured to, if present, prevent the addition of the relevant symptom data to the target disease diagnosis-related group; and if absent, add the relevant symptom data to the target disease diagnosis-related group.

8. An electronic device, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, containing operation instructions for performing the method for obtaining disease diagnosis-related groups as described in any one of claims 1 to 6.

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