CN117438022A - Medical service data processing method, device, equipment and storage medium - Google Patents
Medical service data processing method, device, equipment and storage medium Download PDFInfo
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- 238000003672 processing method Methods 0.000 title claims abstract description 18
- 201000010099 disease Diseases 0.000 claims abstract description 174
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 174
- 238000012216 screening Methods 0.000 claims abstract description 64
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000007689 inspection Methods 0.000 claims abstract description 25
- 238000003745 diagnosis Methods 0.000 claims abstract description 12
- 230000002159 abnormal effect Effects 0.000 claims description 43
- 238000012545 processing Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
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- 238000012360 testing method Methods 0.000 description 3
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- 206010008111 Cerebral haemorrhage Diseases 0.000 description 1
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- 206010037660 Pyrexia Diseases 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 208000004124 rheumatic heart disease Diseases 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The application provides a medical service data processing method, a device, equipment and a storage medium, and relates to the technical field of medical treatment. The method includes acquiring medical treatment data of a plurality of patients in a hospital, wherein the medical treatment data comprises: medical record files and inspection files; adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in the hospital to obtain rule matching results of each patient in the hospital; and determining target patients with the disease risks of the preset disease types from the plurality of patients in the hospital according to the rule matching results of the plurality of patients in the hospital. Therefore, through setting up screening rules, the medical treatment data of the patient at home is analyzed and screened, the disease risk of the patient is accurately screened, the screening precision is improved, the work of a clinician is assisted, the problem of the patient is found in advance, the missed diagnosis risk is reduced, and medical services are provided for the patient more comprehensively.
Description
Technical Field
The present invention relates to the field of medical technology, and in particular, to a medical service data processing method, apparatus, device, and storage medium.
Background
Under the current hospital environment, as people pay more attention to health problems, the concern of being able to predict own physical problems in advance and having diseases is deeper and deeper. The hospital disease screening system can acquire patient inspection results according to patient information to screen diseases, and send contact information and inspection results of target patients to specialists through mails, so that the doctors actively contact the patients for follow-up.
The screening precision of the existing system is low, and diseases of patients are difficult to accurately screen.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a medical service data processing method, a device, equipment and a storage medium, so as to solve the problems of disease screening precision and the like in the prior art.
In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:
in a first aspect, an embodiment of the present application provides a medical service data processing method, where the method includes:
acquiring medical visit data for a plurality of patients at a hospital, the medical visit data comprising: medical record files and inspection files;
adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in a hospital to obtain rule matching results of each patient in the hospital;
and determining target patients with the disease risk of the preset disease species from the plurality of hospital patients according to the rule matching results of the plurality of hospital patients.
Optionally, the screening rule of the preset disease is adopted to match the medical record file and the inspection file of each patient in the hospital, so as to obtain the rule matching result of each patient in the hospital, and before the method further comprises the following steps:
acquiring medical treatment data of a plurality of normal patients and medical treatment data of a plurality of confirmed patients, wherein the normal patients are: a patient diagnosed with the preset disease is not identified, and the diagnosed patient is: identifying a patient diagnosed with the predetermined disease species;
determining candidate rules of the preset disease types from sample rules of the preset disease types according to the medical treatment data of the plurality of normal patients;
and determining screening rules of the preset disease types from candidate rules of the preset disease types according to the medical treatment data of the plurality of diagnosed patients.
Optionally, the determining, according to the medical treatment data of the plurality of normal patients, a candidate rule of a preset disease from sample rules of the preset disease includes:
according to the sample rules of the preset disease types and the medical treatment data of the plurality of normal patients, determining that the patient with abnormal item in the medical treatment data is an abnormal patient, and determining that the rule matched with the abnormal item is a matching rule of the abnormal patient;
and determining candidate rules of the preset disease types according to the matching rules of all the abnormal patients.
Optionally, the determining the candidate rule of the preset disease according to the matching rules of all the abnormal patients includes:
determining that the patients with the number of the matching rules greater than or equal to the preset number are suspected patients from all the abnormal patients;
and determining rules with the occurrence frequency greater than or equal to a preset frequency threshold value from all the matching rules of the suspected patients as the candidate rules.
Optionally, the determining, according to the medical treatment data of the plurality of diagnosed patients, a screening rule of the preset disease from candidate rules of the preset disease includes:
determining the matching rate of the candidate rule according to the medical treatment data of the plurality of diagnosed patients and the candidate rule;
and determining a rule with the matching rate larger than or equal to a preset matching threshold value from the candidate rules of the preset disease seeds as a screening rule of the preset disease seeds according to the matching rate of the candidate rules.
Optionally, the method further comprises:
acquiring medical treatment data of a plurality of newly added diagnosis patients aiming at the preset disease in a preset historical time period;
extracting high frequency content from the medical visit data;
matching the high-frequency content with a screening rule of the preset disease seeds to obtain a matching result of the high-frequency content;
and determining the suggestion rule of the preset disease according to the matching result of the high-frequency content.
Optionally, the method further comprises:
and storing the high-frequency content, the matching result of the high-frequency content and the suggestion rule into a preset suggestion rule library.
In a second aspect, an embodiment of the present application provides a medical service data processing apparatus, the apparatus including:
an acquisition module for acquiring medical treatment data of a plurality of patients in a hospital, the medical treatment data comprising: medical record files and inspection files;
the matching module is used for respectively matching medical record files and inspection files of each patient in the hospital by adopting screening rules of preset disease types to obtain rule matching results of each patient in the hospital;
and the determining module is used for determining target patients with the disease risk of the preset disease species from the plurality of hospital patients according to the rule matching results of the plurality of hospital patients.
In a third aspect, an embodiment of the present application provides an electronic device, including: the medical service data processing method according to any one of the first aspect comprises a processor and a storage medium, wherein the processor is in communication connection with the storage medium through a bus, the storage medium stores program instructions executable by the processor, and the processor calls a program stored in the storage medium to execute the steps of the medical service data processing method according to any one of the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the medical service data processing method according to any of the first aspects.
Compared with the prior art, the application has the following beneficial effects:
the application provides a medical service data processing method, a device, equipment and a storage medium. The method includes acquiring medical treatment data of a plurality of patients in a hospital, wherein the medical treatment data comprises: medical record files and inspection files; adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in the hospital to obtain rule matching results of each patient in the hospital; and determining target patients with the disease risks of the preset disease types from the plurality of patients in the hospital according to the rule matching results of the plurality of patients in the hospital. Therefore, through setting up screening rules, the medical treatment data of the patient at home is analyzed and screened, the disease risk of the patient is accurately screened, the screening precision is improved, the work of a clinician is assisted, the problem of the patient is found in advance, the missed diagnosis risk is reduced, and medical services are provided for the patient more comprehensively.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a medical service data processing method provided in the present application;
FIG. 2 is a flowchart of a method for generating screening rules for a preset disease seed according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for determining candidate rules of a preset disease type according to an embodiment of the present application;
fig. 4 is a flowchart of a method for determining candidate rules of a preset disease from matching rules of all abnormal patients according to an embodiment of the present application;
FIG. 5 is a flowchart of a method for determining a screening rule from candidate rules according to an embodiment of the present application;
fig. 6 is a flowchart of a method for generating a suggestion rule of a preset disease seed according to an embodiment of the present application;
fig. 7 is a schematic diagram of a medical service data processing device according to an embodiment of the present application;
fig. 8 is a schematic diagram of an electronic device according to an embodiment of the present application.
Icon: 701-acquisition module, 702-matching module, 703-determination module, 801-processor, 802-storage medium.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
In order to improve the screening precision of patients suffering from diseases, the application provides a medical service data processing method, a device, equipment and a storage medium.
A medical service data processing method provided in the present application is explained by a specific example as follows. Fig. 1 is a schematic flow chart of a medical service data processing method provided in the present application, where an execution subject of the method is an electronic device, and the electronic device has a computing processing function. As shown in fig. 1, the method includes:
s101, acquiring medical treatment data of a plurality of patients in a hospital.
The medical visit data includes: medical record file and inspection test file. The medical record file includes: main complaints, major diagnoses, current medical history, past history, family history, physical examination, and the like. Checking the verification document includes: checking the result and the test result.
Specifically, in order to timely process the medical service data of the patient, the disease type of the patient is screened, and the medical treatment data of all the patients still in hospital are acquired at preset time of each day. For example, medical visit data for a plurality of patients in a hospital are acquired at 0 and 12 hours a day.
S102, adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in the hospital, and obtaining rule matching results of each patient in the hospital.
Wherein the preset disease is specific disease or symptom, such as coronary heart disease, hypertension, rheumatic heart disease, cerebral hemorrhage, etc. The preset disease comprises a plurality of disease so as to match the patients in the hospital by adopting screening rules of the plurality of disease. For example, a patient in a hospital may not match any one preset disease, may match one preset disease, or may match multiple preset disease.
Illustratively, the screening rules for each disease species include at least one rule, each rule including at least one rule condition. The rule condition may be a condition corresponding to a medical record file (e.g., having a history of a disease, fever, etc.), or a condition corresponding to a check file (e.g., an a index being greater than a certain threshold, a B index being lower than a certain threshold, etc.). Specifically, rule 1 may be: condition 1+condition 2+condition 3, rule 2 may be: condition 4+ condition 5, rule 3 may be: condition 6.
And when the medical record file and the check and inspection file of the hospital patient are successfully matched with at least one rule in a certain preset disease, determining that the hospital patient has the disease risk of the preset disease. Wherein, the matching is considered successful when the matching is matched with all rule conditions in one rule.
S103, determining target patients with the disease risks of preset disease types from the plurality of hospital patients according to rule matching results of the plurality of hospital patients.
The rule matching result contains preset disease types matched with patients in hospital. According to the rule matching result of a plurality of patients in the hospital, the patient in the hospital with the disease risk of the preset disease is determined as the target patient, and the matched preset disease of each target patient is displayed.
Further, for patients in the hospital who do not have the risk of the preset disease, the preset disease is not treated; and for the target patient, pushing the preset disease types matched with the target patient and the medical treatment data to a doctor client corresponding to the preset disease types. And the doctor checks the medical treatment data matched with the target patient at the client side to screen, and inputs treatment advice at the doctor client side. The abatement recommendations include: recommended medication inspection, recommended consultation, recommended diversion, and exclusion of suspected non-sickness. After receiving the treatment advice sent by the doctor-specific client, the treatment advice is sent to the doctor-specific client of the target patient, so that the doctor-specific client can perform further treatment according to the treatment advice.
To sum up, in the present embodiment, a plurality of medical treatment data of patients in hospital are acquired, and the medical treatment data includes: medical record files and inspection files; adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in the hospital to obtain rule matching results of each patient in the hospital; and determining target patients with the disease risks of the preset disease types from the plurality of patients in the hospital according to the rule matching results of the plurality of patients in the hospital. Therefore, through setting up screening rules, the medical treatment data of the patient at home is analyzed and screened, the disease risk of the patient is accurately screened, the screening precision is improved, the work of a clinician is assisted, the problem of the patient is found in advance, the missed diagnosis risk is reduced, and medical services are provided for the patient more comprehensively.
On the basis of the embodiment corresponding to fig. 1, the embodiment of the application also provides a method for generating screening rules of preset disease types. Fig. 2 is a flowchart of a method for generating screening rules of a preset disease seed according to an embodiment of the present application. As shown in fig. 2, in S102, the screening rules of the preset disease types are adopted to match the medical record file and the inspection file of each patient in the hospital, so as to obtain the rule matching result of each patient in the hospital, and before the method further includes:
s201, medical treatment data of a plurality of normal patients and medical treatment data of a plurality of confirmed patients are acquired.
Wherein, normal patients are: patients diagnosed with the preset disease species were not confirmed, and the confirmed patients were: patients diagnosed with the preset disease species are identified.
Illustratively, taking the A disease as an example, from all sample patients, 1000 normal patients and 100 definitive patients diagnosed with the A disease are selected. And acquiring medical visit data of 1000 normal patients and medical visit data of 100 confirmed patients.
S202, determining candidate rules of preset disease types from sample rules of the preset disease types according to medical treatment data of a plurality of normal patients.
The preselection is based on a plurality of sample rules set by a physician based on diagnostic experience.
Firstly, medical treatment data of a normal patient are adopted, matching is carried out from sample rules of preset disease types, and the matched rules are determined to be candidate rules of the preset disease types.
S203, determining screening rules of preset disease types from candidate rules of the preset disease types according to medical treatment data of a plurality of diagnosed patients.
And matching the medical treatment data of the confirmed patient from candidate rules of the preset disease types, and determining the matched rules as screening rules of the preset disease types.
The screening rules of each preset disease species are determined by adopting the flow.
For example, at intervals, the screening rules of the preset disease seeds may also be regenerated to improve the accuracy of the screening rules.
To sum up, in the present embodiment, medical treatment data of a plurality of normal patients and medical treatment data of a plurality of diagnosed patients are acquired, wherein the normal patients are: patients diagnosed with the preset disease species were not confirmed, and the confirmed patients were: confirming a patient diagnosed with a preset disease; according to the medical treatment data of a plurality of normal patients, determining candidate rules of preset disease types from sample rules of the preset disease types; and determining screening rules of the preset disease types from candidate rules of the preset disease types according to medical treatment data of a plurality of diagnosed patients. Therefore, screening rules of preset disease types are accurately generated according to the medical treatment data.
On the basis of the embodiment corresponding to fig. 2, the embodiment of the application also provides a method for determining candidate rules of preset disease types. Fig. 3 is a flowchart of a method for determining candidate rules of a preset disease type according to an embodiment of the present application. As shown in fig. 2, determining a candidate rule of a preset disease from sample rules of the preset disease according to medical visit data of a plurality of normal patients in S202 includes:
s301, determining that a patient with an abnormal item in medical treatment data is an abnormal patient according to sample rules of preset disease types and medical treatment data of a plurality of normal patients, and determining that a rule matched with the abnormal item is a matching rule of the abnormal patient.
If the medical treatment data of the normal patient is matched with at least one rule in the sample rules and abnormal items in the rule exist, determining that the normal patient possibly has a preset disease type, and taking the patient as an abnormal patient. And determining that the rule matching the abnormal item is a matching rule of the abnormal patient, namely, taking the rule matching the medical treatment data of the abnormal patient as the matching rule.
S302, determining candidate rules of preset disease types according to the matching rules of all abnormal patients.
And further screening from the matching rules of all abnormal patients to determine candidate rules of preset disease types.
To sum up, in this embodiment, according to a sample rule of a preset disease and medical treatment data of a plurality of normal patients, determining that a patient having an abnormal item in the medical treatment data is an abnormal patient, and determining that a rule matched with the abnormal item is a matching rule of the abnormal patient; and determining candidate rules of preset disease types according to the matching rules of all abnormal patients. Thus, the candidate rule is accurately determined.
On the basis of the embodiment corresponding to fig. 3, the embodiment of the application also provides a method for determining candidate rules of preset disease types from the matching rules of all abnormal patients. Fig. 4 is a flowchart of a method for determining candidate rules of a preset disease from matching rules of all abnormal patients according to an embodiment of the present application. As shown in fig. 4, determining candidate rules of the preset disease according to the matching rules of all abnormal patients in S302 includes:
s401, determining that patients with the number of the matching rules being greater than or equal to the preset number are suspected patients from all abnormal patients.
Each abnormal patient has at least one matching rule. For example, the preset number may be 2, and the patient with the number of matching rules greater than or equal to 2 is determined to be a suspected patient.
S402, determining rules with occurrence frequency greater than or equal to a preset frequency threshold value as candidate rules from the matching rules of all suspected patients.
Dividing the number of patients containing a certain matching rule by the number of suspected patients to obtain the occurrence frequency of the matching rule.
For example, in addition to the above-described manner of determining the candidate rule, a rule of a preset value before the occurrence frequency arrangement may be determined as the candidate rule. For example, the rule of 10 before the frequency of occurrence is determined as a candidate rule.
To sum up, in the present embodiment, it is determined that the patients with the number of matching rules greater than or equal to the preset number are suspected patients from all abnormal patients; and determining rules with the occurrence frequency greater than or equal to a preset frequency threshold value from the matching rules of all suspected patients as candidate rules. Thus, the candidate rule is accurately determined through the screening twice.
On the basis of the embodiment corresponding to fig. 2, the embodiment of the application also provides a method for determining screening rules from candidate rules. Fig. 5 is a flowchart of a method for determining a screening rule from candidate rules according to an embodiment of the present application. As shown in fig. 5, determining screening rules of a preset disease from candidate rules of the preset disease according to medical visit data of a plurality of diagnosed patients in S203 includes:
s501, determining the matching rate of the candidate rule according to the medical treatment data of a plurality of diagnosed patients and the candidate rule.
For each candidate rule, matching the candidate rule with medical treatment data of a plurality of confirmed patients, and determining the number of successfully matched patients. Dividing the number of successfully matched patients by the total number of diagnosed patients to obtain the matching rate of the candidate rule.
S502, determining a rule with the matching rate larger than or equal to a preset matching threshold value as a screening rule of the preset disease seeds from the candidate rules of the preset disease seeds according to the matching rate of the candidate rules.
For example, the preset matching threshold may be 90%. And taking the rule with the matching rate of more than or equal to 90% as a screening rule of the preset disease seeds.
Since the diagnosed patient has been determined to have the preset disease, all rules for the preset disease should be met. Therefore, the candidate rules are checked by adopting the medical treatment data of the patient with definite diagnosis, and the rules with high matching rate are selected from the candidate rules as screening rules of the preset disease types.
To sum up, in the present embodiment, according to the medical treatment data of a plurality of diagnosed patients and the candidate rule, the matching rate of the candidate rule is determined; and determining a rule with the matching rate larger than or equal to a preset matching threshold value from candidate rules of the preset disease seeds as a screening rule of the preset disease seeds according to the matching rate of the candidate rules. Thus, the screening rules of the preset disease seeds are accurately determined.
On the basis of the embodiment corresponding to fig. 1, the embodiment of the application also provides a method for generating the suggestion rule of the preset disease. Fig. 6 is a flowchart of a method for generating a suggestion rule of a preset disease type according to an embodiment of the present application. As shown in fig. 6, the method further comprises:
s601, acquiring medical treatment data of a plurality of newly added diagnosis patients aiming at a preset disease type in a preset historical time period.
In order to screen the patient for the preset disease seeds more accurately, the screening rules are updated at intervals.
For example, the preset history period may be the previous week, the previous month. The newly added diagnosis patient can be the newly added diagnosis patient of the hospital, and can also be the newly added diagnosis patient of all hospitals applying the medical service data processing method provided by the application.
S602, extracting high-frequency content from medical treatment data.
Illustratively, a word segmentation device is used for word segmentation and frequency analysis of the medical record file. Specifically, the structured medical record contents such as main complaints, main diagnoses, current medical history, past history, family history, physical examination and the like are subjected to word segmentation processing, statistics is carried out to obtain words with higher occurrence frequency, and high-frequency (for example, the frequency is higher than or equal to 20 percent) words are extracted.
For example, the abnormality index in the inspection file is extracted, the frequency of occurrence of the abnormality index is summarized and analyzed, and the high-frequency (for example, the frequency is 20% or more or higher) index is extracted. Wherein the index is also presented in the form of vocabulary.
The high-frequency vocabulary and the high-frequency index are used as the high-frequency content.
S603, matching the high-frequency content with a screening rule of a preset disease type to obtain a matching result of the high-frequency content.
The high frequency content has the same part as the screening rule of the preset disease and also has a difference part. The matching result of the high frequency content includes: the content of the same part, the content of the different part.
S604, determining a suggestion rule of a preset disease type according to a matching result of the high-frequency content.
And determining the recommended rule of the preset disease seeds by the content of the difference part of the high-frequency content and the screening rule of the preset disease seeds. And pushing the screening rules to a doctor client so that the doctor can update and add the screening rules according to the suggested rules of the preset disease types.
To sum up, in this embodiment, medical treatment data of a plurality of newly added diagnosis patients for a preset disease in a preset history period is obtained; extracting high frequency content from medical visit data; matching the high-frequency content with a screening rule of a preset disease type to obtain a matching result of the high-frequency content; and determining a suggestion rule of the preset disease type according to the matching result of the high-frequency content. Thus, suggested rules are generated in time for update additions to the screening rules.
Based on the embodiment corresponding to fig. 6, in another embodiment of the present application, the method further includes:
and storing the high-frequency content, the matching result of the high-frequency content and the suggestion rule into a preset suggestion rule base.
The suggestion rule base is a database, and the high-frequency content, the matching result of the high-frequency content and the suggestion rule are stored in the preset suggestion rule base so as to call the high-frequency content, the matching result of the high-frequency content and the suggestion rule from the suggestion rule base.
To sum up, in the present embodiment, the high frequency content, the matching result of the high frequency content, and the suggestion rule are stored in a preset suggestion rule base. Thus, the recommendation rules are facilitated to be invoked.
The following describes a medical service data processing device, a storage medium, and the like provided in the present application for execution, and specific implementation processes and technical effects thereof are referred to above, which are not described in detail below.
Fig. 7 is a schematic diagram of a medical service data processing device according to an embodiment of the present application, as shown in fig. 7, where the device includes:
an acquisition module 701, configured to acquire medical treatment data of a plurality of patients in a hospital, where the medical treatment data includes: medical record file and inspection test file.
And the matching module 702 is used for respectively matching the medical record file and the inspection file of each patient in the hospital by adopting screening rules of preset disease types to obtain rule matching results of each patient in the hospital.
A determining module 703, configured to determine a target patient with a disease risk of a preset disease type from a plurality of patients in a hospital according to the rule matching results of the plurality of patients in the hospital.
Further, the acquiring module 701 is further configured to acquire medical treatment data of a plurality of normal patients and medical treatment data of a plurality of diagnosed patients, where the normal patients are: patients diagnosed with the preset disease species were not confirmed, and the confirmed patients were: confirming a patient diagnosed with a preset disease; according to the medical treatment data of a plurality of normal patients, determining candidate rules of preset disease types from sample rules of the preset disease types; and determining screening rules of the preset disease types from candidate rules of the preset disease types according to medical treatment data of a plurality of diagnosed patients.
Further, the acquiring module 701 is specifically configured to determine, according to a sample rule of a preset disease and medical treatment data of a plurality of normal patients, that a patient with an abnormal item in the medical treatment data is an abnormal patient, and determine a rule matched with the abnormal item as a matching rule of the abnormal patient; and determining candidate rules of preset disease types according to the matching rules of all abnormal patients.
Further, the acquiring module 701 is specifically configured to determine that, from all abnormal patients, the patients with the number of matching rules greater than or equal to the preset number are suspected patients; and determining rules with the occurrence frequency greater than or equal to a preset frequency threshold value from the matching rules of all suspected patients as candidate rules.
Further, an obtaining module 701, specifically configured to determine a matching rate of the candidate rule according to the medical treatment data of the plurality of diagnosed patients and the candidate rule; and determining a rule with the matching rate larger than or equal to a preset matching threshold value from candidate rules of the preset disease seeds as a screening rule of the preset disease seeds according to the matching rate of the candidate rules.
Further, the acquiring module 701 is further configured to acquire medical treatment data of a plurality of newly added diagnosis patients for a preset disease in a preset historical period; extracting high frequency content from medical visit data; matching the high-frequency content with a screening rule of a preset disease type to obtain a matching result of the high-frequency content; and determining a suggestion rule of the preset disease type according to the matching result of the high-frequency content.
Further, the obtaining module 701 is further configured to store the high frequency content, the matching result of the high frequency content, and the suggestion rule into a preset suggestion rule base.
Fig. 8 is a schematic diagram of an electronic device provided in an embodiment of the present application, where the electronic device may be a device with a computing processing function.
The electronic device includes: a processor 801, and a storage medium 802. The processor 801 and the storage medium 802 are connected by a bus.
The storage medium 802 is used to store a program, and the processor 801 calls the program stored in the storage medium 802 to execute the above-described method embodiment. The specific implementation manner and the technical effect are similar, and are not repeated here.
Optionally, the present invention further provides a storage medium comprising a program, which when executed by a processor is adapted to carry out the above-described method embodiments. In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the invention. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
Claims (10)
1. A medical service data processing method, the method comprising:
acquiring medical visit data for a plurality of patients at a hospital, the medical visit data comprising: medical record files and inspection files;
adopting screening rules of preset disease types to respectively match medical record files and inspection files of each patient in a hospital to obtain rule matching results of each patient in the hospital;
and determining target patients with the disease risk of the preset disease species from the plurality of hospital patients according to the rule matching results of the plurality of hospital patients.
2. The method according to claim 1, wherein the screening rules of the preset disease types are adopted to match medical record files and check-up files of each patient in the hospital respectively, and before the rule matching result of each patient in the hospital is obtained, the method further comprises:
acquiring medical treatment data of a plurality of normal patients and medical treatment data of a plurality of confirmed patients, wherein the normal patients are: a patient diagnosed with the preset disease is not identified, and the diagnosed patient is: identifying a patient diagnosed with the predetermined disease species;
determining candidate rules of the preset disease types from sample rules of the preset disease types according to the medical treatment data of the plurality of normal patients;
and determining screening rules of the preset disease types from candidate rules of the preset disease types according to the medical treatment data of the plurality of diagnosed patients.
3. The method of claim 2, wherein the determining candidate rules for the preset disease from sample rules for the preset disease based on the medical visit data for the plurality of normal patients comprises:
according to the sample rules of the preset disease types and the medical treatment data of the plurality of normal patients, determining that the patient with abnormal item in the medical treatment data is an abnormal patient, and determining that the rule matched with the abnormal item is a matching rule of the abnormal patient;
and determining candidate rules of the preset disease types according to the matching rules of all the abnormal patients.
4. A method according to claim 3, wherein said determining candidate rules for said predetermined disease based on matching rules for all of said abnormal patients comprises:
determining that the patients with the number of the matching rules greater than or equal to the preset number are suspected patients from all the abnormal patients;
and determining rules with the occurrence frequency greater than or equal to a preset frequency threshold value from all the matching rules of the suspected patients as the candidate rules.
5. The method of claim 2, wherein the determining the screening rules for the predetermined disease from the candidate rules for the predetermined disease based on the medical visit data for the plurality of diagnosed patients comprises:
determining the matching rate of the candidate rule according to the medical treatment data of the plurality of diagnosed patients and the candidate rule;
and determining a rule with the matching rate larger than or equal to a preset matching threshold value from the candidate rules of the preset disease seeds as a screening rule of the preset disease seeds according to the matching rate of the candidate rules.
6. The method according to claim 1, wherein the method further comprises:
acquiring medical treatment data of a plurality of newly added diagnosis patients aiming at the preset disease in a preset historical time period;
extracting high frequency content from the medical visit data;
matching the high-frequency content with a screening rule of the preset disease seeds to obtain a matching result of the high-frequency content;
and determining the suggestion rule of the preset disease according to the matching result of the high-frequency content.
7. The method of claim 6, wherein the method further comprises:
and storing the high-frequency content, the matching result of the high-frequency content and the suggestion rule into a preset suggestion rule library.
8. A medical service data processing device, the device comprising:
an acquisition module for acquiring medical treatment data of a plurality of patients in a hospital, the medical treatment data comprising: medical record files and inspection files;
the matching module is used for respectively matching medical record files and inspection files of each patient in the hospital by adopting screening rules of preset disease types to obtain rule matching results of each patient in the hospital;
and the determining module is used for determining target patients with the disease risk of the preset disease species from the plurality of hospital patients according to the rule matching results of the plurality of hospital patients.
9. An electronic device, comprising: a processor, a storage medium, the processor and the storage medium being connected through a bus communication, the storage medium storing program instructions executable by the processor, the processor invoking a program stored in the storage medium to perform the steps of the medical service data processing method according to any of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the medical service data processing method according to any of claims 1 to 7.
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|---|---|---|---|
| CN202311403021.3A CN117438022A (en) | 2023-10-26 | 2023-10-26 | Medical service data processing method, device, equipment and storage medium |
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| CN202311403021.3A CN117438022A (en) | 2023-10-26 | 2023-10-26 | Medical service data processing method, device, equipment and storage medium |
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