Incontinence-related dermatitis risk prediction and management system based on data analysis

Abstract

The invention discloses an incontinence-related dermatitis risk prediction and management system based on data analysis, particularly relates to the field of data processing, and comprises a data acquisition module, a data processing and fusion module, a dynamic monitoring module, a preventive measure generation module and a user visualization and feedback module. The data acquisition module collects physiological sensor data, self reports and nursing records of a user, and sends information from different data sources to the data processing and fusion module; the data processing and fusion module preprocesses and integrates the collected data to construct a comprehensive user portrait, and extracts statistical characteristics from the integrated data; the dynamic monitoring module calculates a risk score and sets a risk score threshold according to the current user portrait and the extracted features, and judges the current risk state of the user; and based on the risk judgment result provided by the dynamic monitoring module, personalized prevention measures are generated, and the health condition, the risk state and the prevention measures of the user are displayed by a chart and a visual component.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to an incontinence-related dermatitis risk prediction and management system based on data analysis. Background Art

[0002] Incontinence-related dermatitis is a common skin disease, especially in bedridden or disabled patients. Eczema and skin irritation caused by incontinence not only affect the patient's physical health, but also their psychological state and quality of life. In clinical practice, the occurrence of incontinence-related dermatitis is often caused by inadequate skin protection measures and improper care measures, as well as insufficient risk assessment. With the advent of an aging society, the incidence of incontinence-related dermatitis continues to rise, so it is particularly important to establish an effective risk prediction and management system.

[0003] In medical care, timely identification and management of the risk of incontinence-related dermatitis can not only reduce patients' suffering, but also reduce medical costs and waste of nursing resources. Existing assessment tools often rely on past clinical experience and lack personalized analysis for specific patients, resulting in risk assessment results that may not be accurate enough and difficult to adapt to changes in the patient's condition. In addition, traditional nurse-patient communication methods may not effectively convey patients' real-time information, leaving nursing staff lacking data support when formulating corresponding preventive measures. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an incontinence-related dermatitis risk prediction and management system based on data analysis to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a risk prediction and management system for incontinence-related dermatitis based on data analysis, comprising a data acquisition module, a data processing and fusion module, a dynamic monitoring module, a preventive measures generation module, and a user visualization and feedback module; The data acquisition module is used to collect the user's physiological sensor data, self-reports and nursing records, and send the information from different data sources to the data processing and fusion module; The data processing and fusion module is used to pre-process and integrate the collected data to construct a comprehensive user portrait, and extract statistical features from the integrated data and store them in a database; The dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and the extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module; The preventive measures generation module generates targeted personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module; The user visualization and feedback module provides a user-friendly interface and displays the user's health status, risk status and preventive measures with charts and visualization components, which can enable the user to intuitively understand his or her health status.

[0006] In a preferred embodiment, the data acquisition module collects the user's physiological sensor data, self-report and nursing records, and sends the information from different data sources to the data processing and fusion module. The specific steps are as follows: Step A1, data collection: the physiological sensor data is collected by the user wearing a wearable device every 30 minutes to collect the user's physiological indicators, including humidity, temperature and pressure data on the skin surface; the self-report is that the user fills out a self-report form through a mobile application to record skin status, incontinence frequency, and nursing behavior information, and establishes an interface with the information system of the nursing institution to extract the user's nursing record, which is a document filled out by the nursing staff, including specific measures for patient care, nursing frequency, and changes in skin health status; Step A2, data transmission: After completing the above operations, the physiological sensor data, self-reports and nursing records are summarized to form a structured user data packet, and the summarized data is transmitted to the data processing and fusion module through the set API. The data packet includes the user ID and indicates the data source, including physiological data, self-reports, and nursing records.

[0007] In a preferred embodiment, the data processing and fusion module preprocesses and integrates the collected data to construct a comprehensive user portrait, extracts important features from the integrated data, and stores them in a database. The specific steps are as follows: Step B1, data preprocessing: receiving the data packet transmitted from the data acquisition module and parsing the entire data packet content, including physiological sensor data, self-report and nursing records, confirming success when receiving the data packet, and promptly feeding back information to the data acquisition module for error handling and retransmission when a transmission error occurs, and checking whether there are duplicate records in the data set. For duplicate data records, perform deletion operations, check each field in the data packet, and handle missing values ​​and abnormal values; Step B2, data integration: matching and integrating data from different sources into a comprehensive user portrait database according to user ID, creating a new user portrait table, and filling the matched data into the user portrait table, further comprising the following steps: Step B201, create a user portrait table: use the user ID as the primary key to integrate information from different data sources into a new user portrait table, including user ID, latest collection time, physiological indicators, self-report information, and nursing records. The mathematical expression is: ,in, represents the user portrait dataset at time t, Indicates the user ID, Indicates the latest collection time. represents the physiological sensor data vector, represents self-reported data, represents the nursing record data, t represents the time point of data collection; Step B202: Use user ID as the key field to match data from different sources. For each user ID, fill in the fields of the user portrait table and store the filled user portrait in the database to ensure that the data can be quickly queried and accessed. After the data is filled, perform data verification to ensure that all fields are correctly filled and there are no omissions. Record any problems in the data filling process to facilitate subsequent audits and corrections. Step B3, feature extraction: extracting statistical features of physiological sensor data, user self-reports and nursing records from the integrated data, further comprising the following steps: Step B301: Assume that the physiological sensor data vector is , self-reported data are , the nursing record data is ,in, is the skin surface humidity, is the skin surface temperature, is the pressure data, It's the skin condition. is the frequency of incontinence, It is nursing behavior information. It is a nursing measure. The frequency of care. is the change in skin health status, and t represents the time point of data collection; Step B302: extract the mean values ​​of skin surface humidity, temperature and pressure from the physiological sensor data vector, wherein the extracted mean values ​​of skin surface humidity, temperature and pressure are respectively , , , the mean of incontinence frequency extracted from the self-report data was , the mean of nursing frequency extracted from nursing record data is , extract the mean of skin health status changes as .

[0008] In a preferred embodiment, the dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and the extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module. The specific steps are as follows: Step C1, risk score calculation: Use a weighted linear model to calculate the risk score for incontinence-related dermatitis. ,in, are the weights of the corresponding features, is the risk score, , , are the mean values ​​of skin surface humidity, temperature and pressure, is the mean incontinence frequency, is the mean frequency of care, is the mean of the changes in skin health status; Step C2: Set the low risk threshold of risk score to , the high risk threshold is , based on the calculated risk score and the set threshold, the user's current risk status is determined: when the risk score is range, indicating low risk; when the risk score is range, indicating medium risk; when the risk score is range, indicating high risk.

[0009] In a preferred embodiment, the preventive measures generation module generates targeted personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module. The specific steps are as follows: Step D1, obtaining the user's current risk status from the dynamic monitoring module, and establishing a preventive measures library for users with different risk statuses. For low-risk users, the preventive measures are basic protection, including regular skin checks and keeping the skin dry and clean; for medium-risk users, the preventive measures are enhanced care, increasing the frequency of care on the basis of basic protective measures, and strengthening skin moisturizing and daily care; for high-risk users, providing professional care intervention, including intensive care and medical consultation; Step D2: Generate a care plan based on the user's risk status and personalized preventive measures, and communicate this information to the caregiver through the user's device.

[0010] In a preferred embodiment, the user visualization and feedback module provides a user-friendly interface, displays the user's health status, risk status and preventive measures with charts and visualization components, and enables the user to intuitively understand his or her health status. The specific steps are as follows: Step E1, interface design: Design an intuitive user interface, including a health status overview area, a risk status graphic area, a preventive measures display area, and a user feedback channel; the health status overview area displays the user's overall health status information, the risk status graphic area displays the user's current risk assessment results and risk level, the preventive measures display area lists personalized preventive measures, including specific operation suggestions and frequency, and the user feedback channel is a channel for providing users with feedback and suggestions to improve interactivity and user participation; Step E2, generate visualization components: use a pie chart in the health status overview area to display the user's overall health score, and display the current value and change trend of each health indicator; display the user's current risk level through color coding in the risk status diagram area, where green indicates low risk, yellow indicates medium risk, and red indicates high risk; display the user's current list of preventive measures in the preventive measures display area in the form of cards, which are divided into basic protection, enhanced care, and professional care categories; add a feedback area to the interface, and by setting a free text box, users can enter their opinions and suggestions to realize a user feedback channel.

[0011] The beneficial effects of the present invention are: the present invention includes a data acquisition module, a data processing and fusion module, a dynamic monitoring module, a preventive measures generation module and a user visualization and feedback module; the data acquisition module collects the user's physiological sensor data, self-report and nursing records, and sends information from different data sources to the data processing and fusion module; the data processing and fusion module preprocesses and integrates the collected data to build a comprehensive user portrait, and extracts statistical features from the integrated data; the dynamic monitoring module calculates the risk score and sets the risk score threshold according to the current user portrait and the extracted features, and judges the user's current risk status; based on the risk judgment results provided by the dynamic monitoring module, generates personalized preventive measures, and displays the user's health status, risk status and preventive measures with charts and visualization components. Each module is interconnected to form a closed loop, which not only supports real-time monitoring and evaluation of the user's health status, but also can generate personalized nursing suggestions, and help users understand and perceive their health status through a visualization interface, and improve their self-management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0014] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0015] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0016] Example 1 This embodiment provides Figure 1 The invention discloses an incontinence-related dermatitis risk prediction and management system based on data analysis, which specifically includes a data acquisition module, a data processing and fusion module, a dynamic monitoring module, a preventive measures generation module, and a user visualization and feedback module; The data acquisition module is used to collect the user's physiological sensor data, self-reports and nursing records, and send the information from different data sources to the data processing and fusion module; The data processing and fusion module is used to pre-process and integrate the collected data to construct a comprehensive user portrait, and extract statistical features from the integrated data and store them in a database; The dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and the extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module; The preventive measures generation module generates targeted personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module; The user visualization and feedback module provides a user-friendly interface and displays the user's health status, risk status and preventive measures with charts and visualization components, which can enable the user to intuitively understand his or her health status.

[0017] In this embodiment, the data acquisition module specifically needs to be explained. The data acquisition module collects the user's physiological sensor data, self-report and nursing records, and sends the information from different data sources to the data processing and fusion module. Through data collection from multiple channels, it is possible to cross-verify information, ensure the accuracy and reliability of the data, and optimize subsequent data processing and analysis. The specific steps are as follows: Step A1, data collection: the physiological sensor data is collected by the user wearing a wearable device every 30 minutes to collect the user's physiological indicators, including humidity, temperature and pressure data on the skin surface; the self-report is that the user fills out a self-report form through a mobile application to record skin status, incontinence frequency, and nursing behavior information, and establishes an interface with the information system of the nursing institution to extract the user's nursing record, which is a document filled out by the nursing staff, including specific measures for patient care, nursing frequency, and changes in skin health status; Step A2, data transmission: After completing the above operations, the physiological sensor data, self-reports and nursing records are summarized to form a structured user data packet, and the summarized data is transmitted to the data processing and fusion module through the set API. The data packet includes the user ID and indicates the data source, including physiological data, self-reports, and nursing records.

[0018] In this embodiment, the data processing and fusion module specifically needs to be explained. The data processing and fusion module pre-processes and integrates the collected data to build a comprehensive user portrait, and extracts important features from the integrated data and stores them in a database, which helps to formulate personalized intervention and prevention measures and improve the effectiveness of nursing. The specific steps are as follows: Step B1, data preprocessing: receiving the data packet transmitted from the data acquisition module and parsing the entire data packet content, including physiological sensor data, self-report and nursing records, confirming success when receiving the data packet, and promptly feeding back information to the data acquisition module for error handling and retransmission when a transmission error occurs, and checking whether there are duplicate records in the data set. For duplicate data records, perform deletion operations, check each field in the data packet, and handle missing values ​​and abnormal values; Step B2, data integration: matching and integrating data from different sources into a comprehensive user portrait database according to user ID, creating a new user portrait table, and filling the matched data into the user portrait table, further comprising the following steps: Step B201, create a user portrait table: use the user ID as the primary key to integrate information from different data sources into a new user portrait table, including user ID, latest collection time, physiological indicators, self-report information, and nursing records. The mathematical expression is: ,in, represents the user portrait dataset at time t, Indicates the user ID, Indicates the latest collection time. represents the physiological sensor data vector, represents self-reported data, represents the nursing record data, t represents the time point of data collection; Step B202: Use user ID as the key field to match data from different sources. For each user ID, fill in the fields of the user portrait table and store the filled user portrait in the database to ensure that the data can be quickly queried and accessed. After the data is filled, perform data verification to ensure that all fields are correctly filled and there are no omissions. Record any problems in the data filling process to facilitate subsequent audits and corrections. Step B3, feature extraction: extracting statistical features of physiological sensor data, user self-reports and nursing records from the integrated data, further comprising the following steps: Step B301: Assume that the physiological sensor data vector is , self-reported data are , the nursing record data is ,in, is the skin surface humidity, is the skin surface temperature, is the pressure data, It's the skin condition. is the frequency of incontinence, It is nursing behavior information. It is a nursing measure. The frequency of care. is the change in skin health status, and t represents the time point of data collection; Step B302: extract the mean values ​​of skin surface humidity, temperature and pressure from the physiological sensor data vector, wherein the extracted mean values ​​of skin surface humidity, temperature and pressure are respectively , , , the mean of incontinence frequency extracted from the self-report data was , the mean of nursing frequency extracted from nursing record data is , extract the mean of skin health status changes as .

[0019] In this embodiment, the dynamic monitoring module specifically needs to be explained. The dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and the extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module. With the help of the scoring results, high-risk users can be quickly identified, so that nursing staff can give priority to these users and enhance the pertinence of nursing. The specific steps are as follows: Step C1, risk score calculation: Use a weighted linear model to calculate the risk score for incontinence-related dermatitis. ,in, are the weights of the corresponding features, is the risk score, , , are the mean values ​​of skin surface humidity, temperature and pressure, is the mean incontinence frequency, is the mean frequency of care, is the mean of the changes in skin health status; Step C2: Set the low risk threshold of risk score to , the high risk threshold is , based on the calculated risk score and the set threshold, the user's current risk status is determined: when the risk score is range, indicating low risk; when the risk score is range, indicating medium risk; when the risk score is range, indicating high risk.

[0020] In this embodiment, it is specifically necessary to explain the preventive measures generation module, which generates targeted personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module, which can effectively reduce the risk of incontinence-related dermatitis and improve the quality of life of users. The specific steps are as follows: Step D1, obtaining the user's current risk status from the dynamic monitoring module, and establishing a preventive measures library for users with different risk statuses. For low-risk users, the preventive measures are basic protection, including regular skin checks and keeping the skin dry and clean; for medium-risk users, the preventive measures are enhanced care, increasing the frequency of care on the basis of basic protective measures, and strengthening skin moisturizing and daily care; for high-risk users, early warning of skin problems, and providing professional care intervention, including intensive care and medical consultation; Step D2: Generate a corresponding care plan based on the user's risk status and personalized preventive measures, and convey this information to the caregiver through the user's device.

[0021] In this embodiment, the user visualization and feedback module specifically needs to be explained. The user visualization and feedback module provides a user-friendly interface, displays the user's health status, risk status and preventive measures with charts and visualization components, and can intuitively let the user understand his or her health status. The specific steps are as follows: Step E1, interface design: Design an intuitive user interface, including a health status overview area, a risk status graphic area, a preventive measures display area, and a user feedback channel; the health status overview area displays the user's overall health status information, the risk status graphic area displays the user's current risk assessment results and risk level, the preventive measures display area lists personalized preventive measures, including specific operation suggestions and frequency, and the user feedback channel is a channel for providing users with feedback and suggestions to improve interactivity and user participation; Step E2, generate visualization components: use a pie chart in the health status overview area to display the user's overall health score, and display the current value and change trend of each health indicator; display the user's current risk level through color coding in the risk status diagram area, where green indicates low risk, yellow indicates medium risk, and red indicates high risk; display the user's current list of preventive measures in the preventive measures display area in the form of cards, which are divided into basic protection, enhanced care, and professional care categories, and are attached with relevant color labels to improve the visualization effect; add a feedback area to the interface, and by setting a free text box, users can enter their opinions and suggestions to realize a user feedback channel.

[0022] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0023] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0024] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0025] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0026] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0027] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0028] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A risk prediction and management system for incontinence-related dermatitis based on data analysis, characterized in that: It includes data acquisition module, data processing and fusion module, dynamic monitoring module, preventive measures generation module, and user visualization and feedback module; The data acquisition module is used to collect the user's physiological sensor data, self-reports and nursing records, and send the information from different data sources to the data processing and fusion module; The data processing and fusion module is used to pre-process and integrate the collected data to construct a comprehensive user portrait, and extract statistical features from the integrated data and store them in a database; The dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and the extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module; The preventive measures generation module generates personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module; The user visualization and feedback module uses charts and visualization components to display the user's health status, risk status and preventive measures.

2. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 1, characterized in that: The data acquisition module collects the user's physiological sensor data, self-reports, and nursing records, and sends the information from different data sources to the data processing and fusion module. The specific steps are as follows: Step A1, data collection: the physiological sensor data is to collect the user's physiological indicators, including humidity, temperature and pressure data on the skin surface; the self-report is a self-report form filled out by the user to record skin status, incontinence frequency, and nursing behavior information; the nursing record is a document filled out by the nursing staff, including specific measures of patient care, nursing frequency, and changes in skin health status; Step A2, data transmission: After completing the above operations, the physiological sensor data, self-reports and nursing records are summarized to form a structured user data packet, and the summarized data is transmitted to the data processing and fusion module through the set API. The data packet includes the user ID and indicates the data source, including physiological data, self-reports, and nursing records.

3. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 2, characterized in that: The data processing and fusion module pre-processes and integrates the collected data to construct a comprehensive user portrait, extracts important features from the integrated data, and stores them in the database. The specific steps are as follows: Step B1, data preprocessing: receiving the data packet transmitted from the data acquisition module, parsing the entire data packet content, including physiological sensor data, self-report and nursing records, and checking whether there are duplicate records in the data set. For duplicate data records, perform deletion operations, check each field in the data packet, and handle missing values ​​and abnormal values; Step B2, data integration: Match and integrate data from different sources into a comprehensive user portrait database based on user ID, create a new user portrait table, and fill the matched data into the user portrait table; Step B3, feature extraction: extract statistical features of physiological sensor data, user self-reports, and nursing records from the integrated data.

4. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 3, characterized in that: In the data integration of step B2, data from different sources are matched and integrated into a comprehensive user portrait database according to user ID, a new user portrait table is created, and the matched data is filled into the user portrait table, further comprising the following steps: Step B201, create a user portrait table: use the user ID as the primary key to integrate information from different data sources into a new user portrait table, including user ID, latest collection time, physiological indicators, self-report information, and nursing records. The mathematical expression is: ,in, represents the user portrait dataset at time t, Indicates the user ID, Indicates the latest collection time. represents the physiological sensor data vector, represents self-reported data, represents the nursing record data, t represents the time point of data collection; Step B202: Use user ID as the key field to match data from different sources. For each user ID, fill in the fields of the user portrait table and store the filled user portrait in the database. After the data is filled, perform data verification to ensure that all fields are correctly filled and there are no omissions.

5. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 3, characterized in that: In the feature extraction of step B3, statistical features of physiological sensor data, user self-report and nursing records are extracted from the integrated data, further comprising the following steps: Step B301: Assume that the physiological sensor data vector is , self-reported data are , the nursing record data is ,in, is the humidity of the skin surface, is the skin surface temperature, is the pressure data, It's the skin condition. is the frequency of incontinence, It is nursing behavior information. It is a nursing measure. The frequency of care. is the change in skin health status, and t represents the time point of data collection; Step B302: extract the mean values ​​of skin surface humidity, temperature and pressure from the physiological sensor data vector, wherein the extracted mean values ​​of skin surface humidity, temperature and pressure are respectively , , , the mean of incontinence frequency extracted from the self-report data was , the mean of nursing frequency extracted from nursing record data is , extract the mean of skin health status changes as .

6. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 1, characterized in that: The dynamic monitoring module calculates the risk score of incontinence-related dermatitis based on the current user portrait and extracted features, sets a threshold for the risk score, determines the user's current risk status, and feeds the result back to the preventive measures generation module. The specific steps are as follows: Step C1, risk score calculation: Use a weighted linear model to calculate the risk score for incontinence-related dermatitis. ,in, are the weights of the corresponding features, is the risk score, , , are the mean values ​​of skin surface humidity, temperature and pressure, is the mean incontinence frequency, is the mean frequency of care, is the mean of the changes in skin health status; Step C2: Set the low risk threshold of risk score to , the high risk threshold is , based on the calculated risk score and the set threshold, the user's current risk status is determined: when the risk score is range, indicating low risk; when the risk score is range, indicating medium risk; when the risk score is range, indicating high risk.

7. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 6, characterized in that: The preventive measures generation module generates personalized preventive measures based on the risk judgment results provided by the dynamic monitoring module. The specific steps are as follows: Step D1, obtaining the user's current risk status from the dynamic monitoring module, and establishing a preventive measures library for users with different risk statuses. For low-risk users, the preventive measures are basic protection, including regular skin checks and keeping the skin dry and clean; for medium-risk users, the preventive measures are enhanced care, increasing the frequency of care on top of basic protective measures, and strengthening skin moisturizing and daily care; for high-risk users, providing professional care intervention, including intensive care and medical consultation; Step D2: Generate a care plan based on the user's risk status and personalized preventive measures, and communicate this information to the caregiver through the user's device.

8. The incontinence-related dermatitis risk prediction and management system based on data analysis according to claim 7, characterized in that: The user visualization and feedback module uses charts and visualization components to display the user's health status, risk status and preventive measures. The specific steps are as follows: Step E1, interface design: Design a user interface, including a health status overview area, a risk status diagram area, a preventive measures display area, and a user feedback channel; Step E2, generate visualization components: use a pie chart in the health status overview area to display the user's overall health score, and display the current value and change trend of each health indicator; display the user's current risk level through color coding in the risk status diagram area, where green indicates low risk, yellow indicates medium risk, and red indicates high risk; display the user's current list of preventive measures in the preventive measures display area in the form of cards, which are divided into basic protection, enhanced care, and professional care categories; and add a feedback area to the interface. By setting a free text box, users can enter suggestions to realize a user feedback channel.

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