CN112562854A - Accurate medical care service recommendation method and system for elderly people - Google Patents
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Abstract
The disclosure provides a method and a system for recommending accurate medical care services for elderly people, which are used for obtaining data of people to be recommended to obtain a data dimension model; performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models; inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value; according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended; according to the method, different dimensionality characteristics of the elderly are screened, the health condition of the people is judged based on the convolutional neural network algorithm model, medical care recommendation service is actively provided for the elderly with poor health condition, special people are identified through the algorithm model, and accurate people butt joint can be provided for social aid and offline old care health service.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a method and a system for recommending accurate medical care services for elderly people.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
At present, the number of the elderly people is rapidly increased year by year in the aging society, but with the increase of the age, the elderly face the decline of the overall health level, a plurality of health problems such as hypertension, heart disease, arthritis and the like occur, and the disease incidence rate is continuously increased.
However, the inventor of the present disclosure finds that medical care services for elderly people have certain problems, and although the elderly people can obtain information needed by themselves through related websites or mobile software, such existing care recommendation services are often not targeted enough, and recommended contents are not precise and detailed enough; meanwhile, it is considered that some elderly people need to actively provide offline medical care services for the elderly people due to the fact that the elderly people cannot obtain medical care information under economic conditions, most of the existing medical care services respond after requirements are provided, and autonomous medical care service recommendation cannot be achieved.
Disclosure of Invention
In order to solve the defects of the prior art, the accurate medical care service recommendation method and system for the elderly are provided, the health conditions of the elderly are judged based on a convolutional neural network algorithm model by screening different dimensional characteristics of the elderly, medical care recommendation service is actively provided for the elderly with poor health conditions, special crowds are identified through the algorithm model, and accurate crowd butt joint can be provided for social aid and offline aged care health service.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
an accurate medical care service recommendation method for elderly people comprises the following steps:
acquiring data of a person to be recommended to obtain a data dimension model;
performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
A second aspect of the present disclosure provides an elderly person accurate medical care service recommendation system.
An elderly accurate medical care service recommendation system, comprising:
a dimensional model building module configured to: acquiring data of a person to be recommended to obtain a data dimension model;
an analytical model building module configured to: performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
a health assessment module configured to: inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
a medical services recommendation module configured to: and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
A third aspect of the present disclosure provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in the elderly person precision medical care service recommendation method according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for recommending accurate medical care services for elderly people according to the first aspect of the present disclosure.
Compared with the prior art, the beneficial effect of this disclosure is:
1. according to the method, the system, the medium or the electronic equipment, different dimensional characteristics are screened out by collecting open data, correlation analysis is carried out on the data, the health level of people is predicted based on the convolutional neural network algorithm model, the health condition of the people is judged more accurately, and meanwhile the normal life of the old people is not influenced.
2. The method, the system, the medium or the electronic equipment disclosed by the disclosure can be used for pushing the list of the elderly people with poor health conditions, the people of the type basically belong to the elderly people with serious diseases or relatively poor economic conditions, the personalized services such as proper health guidance and medical recommendation are recommended for the people of the type, the people are in butt joint with offline community rescue and the like, and the key help of special people is realized.
3. According to the method, the system, the medium or the electronic equipment, disclosed data are disclosed, the health condition of the elderly is predicted based on a convolutional neural network algorithm, accurate medical care recommendation service is provided for people with poor health conditions, the method, the system, the medium or the electronic equipment are more active and accurate, and the people's feeling of acquisition is improved.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart illustrating a method for recommending accurate medical care services for elderly people according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic structural diagram of an elderly people precision medical care service recommendation system provided in embodiment 2 of the present disclosure.
Fig. 3 is a schematic flow chart of a working method of an elderly people accurate medical care service recommendation system according to embodiment 2 of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as shown in fig. 1, an embodiment 1 of the present disclosure provides a method for recommending accurate medical care services for elderly people, including the following steps:
the method comprises the following steps: acquiring a target data set, and establishing a dimensional model, specifically:
after a target data set is obtained, according to personal basic information of ages, sexes and the like of elder people, hospitalizing historical data of hospitalizing cost, hospitalizing frequency and the like, disease information, hospital information and the like, chi-square test is used for screening and determining data dimensionality, chi-square test is used for testing whether variables are related or not, the confidence coefficient is selected to be 95%, chi-square values are calculated, if the chi-square values are large, the variables are placed in a model, and then a dimensionality model is established.
For example, a dimension model is a model built according to dimensions, and is composed of a dimension table and a fact table, for example, in a primary database, there may be a field "zhangsan, 7/3/2020, spends 100 yuan in a certain hospital," and then the dimensions extracted are: time dimension (7/3/2020), place dimension (a certain hospital), and medicine dimension (isatis root), wherein the dimensions form a dimension table, 100 yuan is fact information, and a fact table is formed, and the dimension table and the fact table form a dimension model.
The dimension indexes are specific objects to be analyzed and analyzed data according to actual requirements, and the dimension model indexes are numerical values such as personnel age, medical treatment cost, medical treatment frequency, medical treatment historical data and the like.
The output of the dimension model is actually a model formed by extracting the desired indexes, and as a plurality of indexes in the data are useless, only the indexes which are useful for the next research are selected, and the selected plurality of indexes form the analysis model in the step two.
Step two: on the basis of the dimension model, performing correlation analysis on data, and establishing a plurality of analysis models of disease complications, disease severity, age cure rate and the like, specifically:
and performing data association analysis by adopting an FP-Tree method, scanning data twice, establishing a top item table, reading the sorted data set, inserting the sorted data set into the FP-Tree, and establishing a plurality of analysis models of disease complications, disease severity, age cure rate and the like.
Step three: determining final input data of the algorithm model, performing data preprocessing, sampling and using the convolutional neural network algorithm model to predict and evaluate the current physical health condition of the elderly people, specifically:
determining the influence factor index dimension, the analysis model index, basic information of the insured person, the treatment condition and the past medical data as the input data of the final algorithm model;
aiming at input data, firstly, preprocessing the data, and cleaning abnormal data which are not in accordance with the requirements of model data and have vacant hospitalization date and discharge date;
in addition, the off-the-world persons in the data are labeled as the persons with the worst health condition and the health grade of 0, because the worst persons occupy smaller numbers, the health grade of the elderly persons is predicted by sampling a convolutional neural network algorithm model to judge the health condition of the persons, the health grade is in direct proportion to the health condition, and then the current physical health condition of the elderly persons is evaluated
Step four: the algorithm model calculates a list of elderly people with poor predicted health conditions, constructs a personal portrait according to occupation, diseases, age and the like, and recommends proper personalized services by combining open information data to realize key assistance, specifically:
the algorithm model pushes out a list of old people with poor predicted health conditions and constructs a personal portrait of the old people, and people of the old people, who are old, have serious diseases or have relatively poor economic conditions, are required to be assisted by more care. The special crowd is identified through the algorithm model, and accurate crowd butt joint is provided for local medical institutions, community hospitals, offline endowment services and the like through acquiring crowd address information.
Example 2:
the embodiment 2 of the present disclosure provides an accurate medical care service recommendation system for elderly people, including:
a dimensional model building module configured to: acquiring data of a person to be recommended to obtain a data dimension model;
an analytical model building module configured to: performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
a health assessment module configured to: inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
a medical services recommendation module configured to: and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
More specifically, the system comprises an information base module 101, a data acquisition module 102, a dimension model module 103, an analysis model module 104, a data processing module 105, a health prediction module 106, a personal representation module 107, an accurate service module 108 and other eight modules, as shown in fig. 1, specifically:
the information base module 101 is used for collecting and integrating the opening data of related departments and related business data;
a data acquisition module 102, configured to acquire a target data set, including personal basic information of age, sex, and the like of elderly people, medical history data of medical cost, medical frequency, and the like, disease information, hospital information, and the like;
the dimension model module 103 is used for screening data in the target data set, determining data dimensions and establishing a dimension model;
an analysis model module 104, configured to perform data association analysis by using multiple methods such as FP _ Tree and the like on the basis of the dimension model, and establish multiple analysis models such as disease complications, disease severity, age cure rate and the like;
the data processing module 105 is used for firstly performing data preprocessing on input data and cleaning abnormal data which do not meet the requirements of model data and have vacant hospitalization date and discharge date;
the health prediction module 106 is used for predicting the health grade of the elderly by sampling and using a convolutional neural network algorithm model to judge the health condition of the elderly, and further evaluating the current physical health condition of the elderly;
a personal portrait module 107 for constructing a personal portrait of a target elderly person according to occupation (such as special work), disease, age, etc.;
and the accurate service module 108 is used for identifying special crowds by combining information data of open drugstores, medical institutions and the like through an algorithm model, and providing accurate crowd butt joint for local medical institutions, community hospitals, offline endowment services and the like by acquiring crowd address information.
As shown in fig. 2, the specific working method of the system is as follows:
step 201: basic data information of the aged persons is collected and integrated through the service information base module 101 and is used for accurate medical care recommendation of the aged persons based on a convolutional neural network algorithm in the follow-up process.
Step 202: data required for making psychiatric care recommendations is obtained from the business information base module 102 by the data acquisition module 102.
Step 203: through the dimension model module 103, the information including personal basic information, medical history information and the like is obtained, data is screened, and data dimensions are determined.
Step 204: through the analysis model module 104, a plurality of analysis models such as disease complications, disease severity and age cure rate are established by performing data association analysis by using an FP _ Tree method.
Step 205: the exception data is cleaned up by the data processing module 105.
Step 206: the health of the elderly in the dataset is assessed by the health prediction module 106.
Step 207: a representation of the elderly person is constructed by a personal representation module 107.
Step 208: accurate medical care recommendation service is performed on elderly people through the accurate service module 108.
Example 3:
the present disclosure embodiment 3 provides a computer-readable storage medium on which a program is stored, which when executed by a processor, implements the steps in the advanced aged person precision medical care service recommendation method according to the present disclosure embodiment 1, the steps being:
acquiring data of a person to be recommended to obtain a data dimension model;
performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
Detailed steps are the same as those of the method for recommending accurate medical care services for elderly people provided in embodiment 1.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of being executed on the processor, where the processor implements the steps in the method for recommending accurate medical care services for elderly people according to embodiment 1 of the present disclosure when executing the program, and the steps are as follows:
acquiring data of a person to be recommended to obtain a data dimension model;
performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
Detailed steps and steps of the accurate medical care service recommendation method for elderly people provided in embodiment 1
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. An accurate medical care service recommendation method for elderly people is characterized by comprising the following steps:
acquiring data of a person to be recommended to obtain a data dimension model;
performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
2. The method for recommending accurate medical care services for elderly people according to claim 1, wherein a data dimension model is obtained, specifically:
after the data of the personnel to be recommended are obtained, according to the basic information data, the hospitalizing history data, the disease data and the hospital data of the personnel to be recommended, data dimensionality is determined through chi-square inspection screening, and variables with chi-square values larger than preset values are placed into a data dimensionality model.
3. The method for recommending accurate medical care services for elderly people according to claim 1, wherein a FP _ Tree method is used for data correlation analysis, a top list is established by scanning data twice, a sorted data set is read in and inserted into the FP _ Tree, and a plurality of health analysis models are obtained.
4. The method of advanced age accurate medical care service recommendation according to claim 3, wherein the health analysis models include a disease complication analysis model, a disease severity analysis model, and an age cure rate analysis model.
5. The method for recommending accurate medical care services for elderly people according to claim 1, wherein data preprocessing is performed before inputting data into the convolutional neural network model, specifically:
and cleaning abnormal data which do not meet the requirements of the model data, labeling people who have left the world in the data as the worst health condition, and not recommending the service, wherein the health grade is zero.
6. The method of claim 1, wherein after the people list with health level lower than the predetermined value is obtained, a personal portrait of each person is constructed according to occupation, disease and age of the people to be recommended in the people list, and personalized medical care service is recommended in combination with the open information data.
7. The method for recommending accurate medical care services for elderly people according to claim 1, wherein recommending personalized medical care services specifically is:
by acquiring the address information of the personnel to be recommended, medical care service docking is provided for local medical institutions or community hospitals or offline care services.
8. An accurate medical care service recommendation system for elderly people, comprising:
a dimensional model building module configured to: acquiring data of a person to be recommended to obtain a data dimension model;
an analytical model building module configured to: performing correlation analysis on the data on the basis of the obtained dimension model to obtain a plurality of health analysis models;
a health assessment module configured to: inputting the data dimension model index, the health analysis model index and personal information data of a person to be recommended into a preset convolutional neural network model to obtain a person list with a health grade lower than a preset value;
a medical services recommendation module configured to: and according to the occupation, the diseases and the ages of the people to be recommended on the personnel list, recommending the medical care service of the people to be recommended.
9. A computer-readable storage medium on which a program is stored, the program, when being executed by a processor, implementing the steps in the elderly person precision medical care service recommendation method according to any of claims 1-7.
10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps in the method for advanced precision medical care service recommendation for elderly persons according to any of claims 1-7.
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| CN113823410A (en) * | 2021-09-24 | 2021-12-21 | 联仁健康医疗大数据科技股份有限公司 | Information recommendation method and device, storage medium and electronic equipment |
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