Multi-dimensional health indicator quantification assessment and report generation system

Abstract

This invention discloses a multi-dimensional health indicator quantitative assessment and report generation system, belonging to the field of medical information processing technology. The system includes a data acquisition unit, a data preprocessing unit, a multi-dimensional indicator quantification unit, a comprehensive health assessment unit, a report generation unit, and a data storage and interaction unit, all connected in sequence. The data acquisition unit collects raw, multi-dimensional health-related data from users. The data preprocessing unit cleans, deduplicates, removes outliers, and standardizes the collected raw data. The comprehensive health assessment unit is equipped with an adaptive weighted assessment algorithm based on preset benchmark weights. Through the collaborative design of these units, this invention achieves multi-dimensional, precise, and personalized health assessment and report generation. Combined with a standardized preprocessing process, it ensures comprehensive, accurate, and complete data, providing reliable data support for accurate assessment and avoiding misjudgments caused by biased evaluation.

Description

Technical Field

[0001] This invention relates to the field of medical information processing technology, and more specifically, to a system for quantitative assessment and report generation of multi-dimensional health indicators. Background Technology

[0002] With the increasing health awareness of residents and the development of medical information technology, health assessment has become an important part of personal health management and medical services. By collecting, analyzing, and evaluating various health indicators, potential health risks can be identified in a timely manner, providing data support for health intervention and medical diagnosis and treatment. Currently, various health assessment-related systems exist on the market, but they still have many shortcomings in practical applications and are unable to meet the needs of multi-dimensional, precise, and personalized health assessments.

[0003] Most existing health assessment systems suffer from a lack of simplistic health indicator dimensions. They primarily focus on collecting and evaluating physiological signs, neglecting the impact of lifestyle, clinical examinations, and psychological state on health status. This results in biased assessments that fail to comprehensively reflect a user's true health level. Furthermore, the quantitative rules of existing systems are often fixed and cannot be dynamically adjusted based on the latest medical guidelines, clinical data, and the characteristics of different population groups. This leads to poor adaptability and makes it difficult to meet the differentiated quantitative assessment needs of people of different ages and health conditions.

[0004] Regarding health assessment algorithms, existing systems mostly employ fixed-weight assessment methods, failing to adaptively adjust the weight coefficients of each dimension indicator based on the user's actual health status. This results in insufficient accuracy of assessment results; for example, they cannot provide targeted assessments for different groups such as those with chronic diseases or those in a sub-healthy state. Furthermore, the report generation functions of existing systems are relatively limited, often only generating simple reports in a fixed format. They do not support user-defined report templates or output formats, and the report content lacks focused analysis of abnormal indicators and personalized health intervention suggestions, thus limiting their practicality.

[0005] In addition, the existing system has shortcomings in terms of data processing rigor and data security. The handling of outliers and missing data during data preprocessing is not standardized enough, which can easily lead to biased evaluation results. At the same time, the storage of user health data and evaluation reports often lacks a sound encryption protection and hierarchical access control mechanism, which poses a risk of user privacy leakage.

[0006] In view of the shortcomings of the existing technologies, there is an urgent need for a multi-dimensional health indicator quantitative assessment and report generation system that can accurately collect and quantify multi-dimensional health data, has adaptive weighted assessment capabilities, can generate personalized and standardized assessment reports, and can ensure data security. This system would solve the problems of one-sided assessment, low accuracy, poor adaptability, insufficient practicality, and lack of data security in the existing technologies, and meet the health assessment needs of individuals and medical institutions. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing health assessment systems, such as single dimensions, low assessment accuracy, poor adaptability, and insufficient data security, and to provide a multi-dimensional health indicator quantitative assessment and report generation system to achieve comprehensive, accurate, and personalized health assessment and standardized report output.

[0008] To achieve the above objectives, the present invention provides the following technical solution: The multi-dimensional health indicator quantitative assessment and report generation system includes a data acquisition unit, a data preprocessing unit, a multi-dimensional indicator quantification unit, a comprehensive health assessment unit, a report generation unit, and a data storage and interaction unit, which are connected in sequence via communication. The data acquisition unit is used to collect users' multi-dimensional health-related raw data, which includes at least three of the following: physiological signs data, lifestyle data, clinical examination data, and psychological state data. The data preprocessing unit is used to clean, deduplicate, remove outliers, and standardize the collected raw data to obtain standardized data that can be used for quantitative processing. The multi-dimensional indicator quantification unit has a pre-set indicator quantification rule library based on medical standards. The quantification rule library contains quantification models corresponding to different dimensions of health indicators, which are used to transform standardized data into comparable and calculable quantitative scores, and complete the independent quantification of each dimension of health indicators. The comprehensive health assessment unit is equipped with an adaptive weighted assessment algorithm. The adaptive weighted assessment algorithm is based on a preset benchmark weight and automatically adjusts the weight coefficients of each dimension indicator in combination with the user's health status. It is used to calculate the user's comprehensive health score based on the quantitative scores of each dimension, classify health levels, and identify abnormal indicators and potential health risks. The report generation unit is used to automatically generate standardized and structured health assessment reports based on comprehensive health assessment results, quantitative details of each dimension, abnormal indicator analysis, and health intervention recommendations, according to a preset template. The data storage and interaction unit is used to store all raw data, processed data, quantification results, and evaluation reports, and provides data query, report export, access control, and external terminal interaction functions.

[0009] Preferably, the data acquisition unit includes a built-in acquisition module and an external interface module; the built-in acquisition module is used to directly acquire the user's basic physiological characteristics data; the external interface module supports connection with wearable devices, medical testing instruments, electronic health record systems, and health management platforms to achieve automatic synchronization and real-time acquisition of health data from multiple channels.

[0010] Preferably, the outlier removal in the data preprocessing unit adopts a combination of the 3σ principle and manual verification. The original data that exceeds the normal range is marked, reviewed, and removed after being confirmed as invalid. At the same time, missing data is supplemented by linear interpolation to ensure data integrity.

[0011] Preferably, the quantitative rule base of the multi-dimensional indicator quantification unit supports dynamic updates. Based on the latest medical guidelines, clinical data and population characteristics, the quantitative thresholds, scoring standards and quantitative models of each dimension indicator can be adjusted through the back-end management terminal to adapt to the differentiated quantitative needs of different age groups, genders and occupations.

[0012] Preferably, the adaptive weighted assessment algorithm of the comprehensive health assessment unit can automatically adjust the weight coefficients of each dimension indicator according to the user's health status. Among them, the clinical examination data of people with chronic diseases has a higher weight than that of the general population, and the lifestyle data of people with sub-health has a higher weight than that of the general population.

[0013] Preferably, the comprehensive health assessment unit also includes a risk prediction module. Based on historical health data, current quantitative results, and clinical big data, the risk prediction module uses a random forest machine learning algorithm to predict the user's health risk trend over a future period and outputs targeted risk warning information.

[0014] Preferably, the report generation unit includes a template customization module and a report optimization module; the template customization module allows users or administrators to customize the content modules, layout format, and output format of the report; the report optimization module is used to perform syntax verification and logical organization on the generated report to ensure that the report content is accurate, well-organized, and easy to understand.

[0015] Preferably, the data storage and interaction unit adopts encrypted storage technology to de-identify and encrypt user personal identity information, health data and assessment reports, and sets up a hierarchical permission management mechanism, which only authorizes users, medical staff and system administrators to view and modify the corresponding data according to their permissions, so as to ensure data security and privacy.

[0016] Preferably, it also includes a health intervention recommendation unit, which is communicatively connected to the comprehensive health assessment unit and has a preset health intervention rule base. This rule base is used to automatically match personalized health intervention plans based on the user's comprehensive health score, health level, abnormal indicators, and risk warning information, combined with the diet, exercise, rest, and medical visit related rules in the intervention rule base. These plans include dietary recommendations, exercise plans, rest adjustment suggestions, and medical visit prompts.

[0017] Preferably, the multi-dimensional indicator quantification unit also includes an indicator importance ranking module, which is used to rank all quantitative indicators based on the degree of influence of each dimension indicator on the comprehensive health score, and highlight the core impact indicators in the evaluation report to provide key directions for users' health improvement.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention achieves multi-dimensional, precise, and personalized health assessment and report generation through the collaborative design of each unit. It collects data from multiple channels, covering physiological, lifestyle, clinical, and psychological health data. Combined with a standardized preprocessing process, it ensures comprehensive, accurate, and complete data, providing reliable data support for precise assessment and avoiding misjudgments caused by biased assessments. A quantitative rule base is constructed based on medical standards and supports dynamic updates, adapting to the needs of different populations and the latest medical guidelines. Simultaneously, by ranking the importance of indicators, it helps users quickly identify core health influencing factors, improving the targeting of health management. Personalized and precise health assessment is achieved through an adaptive weighted assessment algorithm that dynamically adjusts indicator weights according to the user's health status. Combined with a random forest algorithm, it enables health risk prediction, providing early warnings of potential health risks and solving the problem of insufficient accuracy in fixed-weight assessments, thus providing a basis for prospective health management.

[0019] (2) The report generation unit of this invention supports template customization and multi-format output, optimizes report readability, eliminates the need for manual report writing, and significantly improves efficiency; the health intervention recommendation unit generates specific and actionable personalized solutions, reducing the difficulty for users to improve their health. It ensures user data security and privacy by using encrypted storage, desensitization processing, and hierarchical permission management to strictly protect users' personal information and health data, complying with relevant laws and regulations and avoiding the risk of privacy leaks. It comprehensively improves the efficiency and value of health management, achieving full automation from data collection, preprocessing, quantitative assessment, risk warning, to report generation and intervention recommendation, reducing labor costs, adapting to the needs of individuals, medical personnel, health management institutions, and other scenarios, and contributing to scientific health management. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of the multi-dimensional health indicator quantitative assessment and report generation system of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0022] Example: Please see Figure 1 The multi-dimensional health indicator quantitative assessment and report generation system includes a data acquisition unit, a data preprocessing unit, a multi-dimensional indicator quantification unit, a comprehensive health assessment unit, a report generation unit, and a data storage and interaction unit, which are connected in sequence via communication. The data acquisition unit is used to collect users' multi-dimensional health-related raw data, which includes at least three of the following categories: physiological signs data, lifestyle data, clinical examination data, and psychological state data. The data acquisition unit includes a built-in acquisition module and an external interface module. By combining built-in acquisition with external interface, it achieves comprehensive and efficient collection of multi-dimensional health data, covering multiple dimensions such as physiological, clinical, and lifestyle aspects, avoiding the data bias caused by a single acquisition channel. The built-in acquisition module is used to directly collect users' basic physiological signs data. The external interface module supports interface with wearable devices, medical testing instruments, electronic health record systems, and health management platforms to achieve automatic synchronization and real-time collection of health data from multiple channels.

[0023] Specifically, the data acquisition unit adopts a modular design, with an integrated acquisition module that combines a heart rate sensor, blood pressure acquisition module, body temperature sensor, and basic information input module. It can directly collect basic physiological data such as heart rate, blood pressure, body temperature, height, and weight. The acquisition frequency can be set in the background (the default is to collect physiological data once every 5 minutes, and users can manually adjust it to real-time or timed acquisition). The external interface module adopts a standardized interface design, supporting multiple interface protocols such as Bluetooth 5.0, USB 3.0, and HTTP / HTTPS. It can seamlessly connect with mainstream wearable devices (such as smart bracelets and smartwatches), medical testing instruments (such as blood glucose meters and blood lipid analyzers), electronic health record (EHR) systems, and third-party health management platforms. During the connection process, an interface encryption and verification mechanism is used to ensure the legality and integrity of data transmission, realizing automatic synchronization of health data from multiple channels (the synchronization frequency can be set to real-time synchronization or once per hour) and real-time acquisition, eliminating the need for manual data entry. The standardized interface design enhances the system's compatibility, making it compatible with different brands and types of data acquisition devices and systems. This reduces the manual cost of data collection, while the real-time synchronization mechanism ensures the timeliness of the data, providing the latest and most comprehensive raw data support for subsequent quantitative evaluation.

[0024] The data preprocessing unit cleans, deduplicates, removes outliers, and standardizes the collected raw data to obtain standardized data suitable for quantitative processing. This effectively removes invalid and redundant data, supplements missing data, and ensures data integrity, accuracy, and consistency. Outlier removal in the preprocessing unit combines the 3σ principle with manual verification. Raw data exceeding the normal range is marked, reviewed, and removed after confirmation of invalidity. Simultaneously, missing data is supplemented using linear interpolation to ensure data integrity. This outlier handling method, combining the 3σ principle with manual verification, avoids the loss of valid data due to algorithmic misjudgment and eliminates interference from outliers in subsequent quantitative evaluation results. Standardization transformation enables unified comparison of data from different dimensions and scales, providing a reliable data foundation for multi-dimensional indicator quantification and significantly improving the accuracy of subsequent evaluation results.

[0025] Specifically, the data preprocessing unit adopts a combination of automated processing and manual review. First, the collected raw data is cleaned to remove invalid data with format errors or data anomalies (such as a heart rate of 0 or blood pressure exceeding the reasonable range of 0-250 mmHg). Deduplication is performed using a hash value comparison algorithm to remove duplicate data from the same dimension and time point collected repeatedly, retaining the latest valid data. Outlier removal strictly follows the 3σ principle: the mean μ and standard deviation σ of each dimension are calculated first, and data exceeding the range of μ±3σ are marked as outliers. The backend administrator performs manual review within 24 hours, and data that is confirmed to be invalid is removed. If the data is confirmed to be valid after review (such as abnormal signs in special pathological states), the data is retained and the reason for the anomaly is marked. Missing data is supplemented using linear interpolation, that is, based on two adjacent valid data points before and after the missing data, the missing value is calculated linearly according to the time series to ensure the continuity of the data. Standardization transformation uses a min-max normalization algorithm to transform data of different magnitudes in each dimension to a unified interval of [0,100], obtaining standardized data that can be used for quantitative processing.

[0026] The multi-dimensional indicator quantification unit has a pre-set indicator quantification rule library based on medical standards. This rule library contains quantification models corresponding to different dimensions of health indicators, used to transform standardized data into comparable and calculable quantitative scores. This enables independent quantification of each dimension of health indicators, ensuring the scientific and rational nature of the quantification and avoiding subjectivity in quantification standards. The multi-dimensional indicator quantification unit's rule library supports dynamic updates. Based on the latest medical guidelines, clinical data, and population characteristics, the quantification thresholds, scoring standards, and quantification models for each dimension can be adjusted through the backend management interface. This adapts to the differentiated quantification needs of different age groups, genders, and occupations. The dynamic update mechanism allows the system to adapt to the development of medical technology and the differentiated needs of different populations, improving the system's versatility and applicability. The indicator importance ranking module helps users quickly identify core indicators affecting their health, clarify key areas for health improvement, and enhance the system's practicality and guidance.

[0027] In this application, the multi-dimensional indicator quantification unit also includes an indicator importance ranking module. This module ranks all quantified indicators based on their impact on the overall health score, and highlights the core influencing indicators in the evaluation report, providing users with key directions for health improvement. In this embodiment, the indicator importance ranking results are arranged from highest to lowest impact. The top three core indicators are highlighted in bold red in the evaluation report, along with the percentage of each indicator's impact on the overall score, allowing users to intuitively understand the key directions for health improvement.

[0028] The quantitative rule base of the multi-dimensional indicator quantification unit is built based on medical standards such as the "Chinese Health Management Standards" and the "Industry Standards for the Interpretation of Clinical Laboratory Results." It includes quantitative models for over 50 specific indicators across four dimensions: physiological signs, lifestyle, clinical examination, and psychological state. Physiological signs (such as blood pressure and heart rate) use a numerical interval quantification model, assigning values ​​according to the data range (e.g., systolic blood pressure of 120-139 mmHg is assigned a score of 80-90). Lifestyle indicators (such as exercise frequency and sleep patterns) use a frequency-level quantification model, assigning values ​​according to behavioral frequency (e.g., exercising 3-5 times per week is assigned a score of 85). Clinical examination indicators (such as blood glucose and blood lipids) use a reference value comparison quantification model, assigning values ​​according to the degree of deviation from the reference value. Psychological state indicators (such as anxiety)... The assessment of depression uses a quantitative model based on a scale, combining the results of psychological scales filled out by users. The quantitative rule base supports dynamic updates, with an update entry point set up in the backend management interface. Administrators can manually adjust the quantitative thresholds, scoring standards, and quantitative models of each dimension indicator based on the latest medical guidelines, clinical data, and characteristics of different populations (age group, gender, occupation). They can also set up automatic monthly synchronization of the latest medical standards to achieve dynamic adaptation of the rule base. The indicator importance ranking module uses the analysis of variance (ANOVA) algorithm to calculate the degree of influence of each dimension quantitative indicator on the comprehensive health score (the larger the variance, the higher the degree of influence). All quantitative indicators are ranked, and the ranking results are displayed from high to low according to their weight. Core influencing indicators are highlighted in bold and red in the assessment report.

[0029] The comprehensive health assessment unit is equipped with an adaptive weighted assessment algorithm. This algorithm automatically adjusts the weight coefficients of each dimension's indicators based on preset baseline weights and the user's health status. These weight coefficients are used to calculate the user's comprehensive health score, classify health levels, and identify abnormal indicators and potential health risks. The adaptive weighted assessment algorithm solves the accuracy problem caused by fixed-weight assessments, achieving personalized and precise comprehensive health assessments. Specifically, the adaptive weighted assessment algorithm automatically adjusts the weight coefficients of each dimension's indicators according to the user's health status (general health, sub-health, chronic disease, high-risk groups). Clinical examination data for chronic disease groups has a higher weight than for the general population, and lifestyle data for sub-health groups has a higher weight. The comprehensive health assessment unit also includes a risk prediction module. Based on historical health data, current quantitative results, and clinical big data, this module uses a random forest machine learning algorithm to predict the user's health risk trends over a future period and outputs targeted risk warnings. This early warning system provides users and medical personnel with a forward-looking basis for health management, reducing the probability of health risk deterioration. The risk prediction module outputs information including risk type, risk probability, risk cycle, and coping suggestions. For example, "The probability of elevated blood lipids in the next 3 months is 68%, and it is recommended to test blood lipids every 2 months." The warning information is also embedded in the assessment report and pushed to the user's terminal.

[0030] Specifically, the adaptive weighted assessment algorithm for the comprehensive health assessment unit has preset baseline weights (physiological signs 30%, lifestyle 25%, clinical examination 30%, and psychological state 15%). These baseline weights are set based on medical research data and can be fine-tuned by administrators in the backend. The algorithm automatically adjusts the weight coefficients of each dimension indicator by identifying the user's health status (based on a comprehensive judgment of clinical examination data, physiological sign data, and the user's completed health questionnaire). For individuals with chronic diseases (such as hypertension and diabetes), the weight of clinical examination data is adjusted to 40%, the weight of physiological sign data to 35%, and the weights of lifestyle and psychological state to 12.5% ​​each. For individuals in a sub-healthy state, the weight of lifestyle data is adjusted to 35%, the weight of physiological sign data to 30%, and the weights of clinical examination and psychological state to 17.5% each. The algorithm uses a weighted summation formula (comprehensive health assessment) based on the quantified scores of each dimension and the adjusted weight coefficients. The health score is calculated as Σ(quantitative score of each dimension × corresponding weight coefficient). Five health levels are divided into score ranges (90-100 for excellent, 80-89 for good, 70-79 for average, 60-69 for sub-health, and below 60 for high risk). Abnormal indicators (quantitative scores below 60) are identified through threshold comparison, and potential health risks are analyzed using clinical big data. The risk prediction module, based on the user's historical health data (last 6 months to 3 years), current quantitative results, and clinical big data, uses a random forest machine learning algorithm (100 decision trees, 80% feature sampling, and 70% sample sampling) to predict the user's health risk trends over the next 3, 6, and 12 months (such as worsening of chronic diseases, sub-health turning into high risk, etc.), and outputs targeted risk warning information (e.g., "High risk of elevated blood sugar in the next 6 months; it is recommended to test blood sugar once a month").

[0031] The report generation unit automatically generates standardized and structured health assessment reports based on comprehensive health assessment results, quantitative details across various dimensions, abnormal indicator analysis, and health intervention recommendations, following preset templates. Diverse preset templates and customization options cater to the varied needs of different users (individuals, healthcare professionals, and health management institutions). The automated report generation process significantly improves efficiency, avoiding the tediousness and errors of manual writing. The report optimization module enhances readability and professionalism, clearly marking abnormal indicators to facilitate quick understanding of one's health status and providing clear guidance for health interventions.

[0032] The report generation unit includes five built-in preset templates (Basic Simplified Version, Detailed Professional Version, Chart Visualization Version, Medical and Healthcare Version, and Personalized Version). The report generation process involves retrieving comprehensive health assessment results, quantitative details of each dimension, abnormal indicator analysis, and health intervention suggestions, and automatically filling in the content according to the layout logic of the preset templates. The template customization module allows users or administrators to customize the report's content modules (such as whether to display historical data comparisons, whether to add health suggestions), layout format (font, line spacing, header and footer), and output format (text version is in PDF format, chart version includes line charts, bar charts, radar charts, etc., and supports Excel export; simplified version is plain text with a simple layout, and detailed version includes complete data and analysis). The report optimization module uses Natural Language Processing (NLP) algorithms to perform grammatical verification and logical streamlining on the generated report, correcting grammatical errors and adjusting sentence coherence to ensure that the report content is accurate, clear, and easy to understand. It also automatically marks abnormal indicators and corresponding risk warnings, making it convenient for users to quickly grasp key information.

[0033] In this application, the data storage and interaction unit is used to store all raw data, processed data, quantification results, and evaluation reports, and provides data query, report export, access control, and external terminal interaction functions.

[0034] The data storage and interaction unit employs encrypted storage technology to de-identify and encrypt user personal information, health data, and assessment reports. It also features a tiered access control mechanism, allowing only authorized users, medical staff, and system administrators to view and modify the corresponding data according to their permissions, thus ensuring data security and privacy.

[0035] The hierarchical access control system is implemented as follows: User-side: Users can only view their own data, export reports, and receive intervention plans; Healthcare professionals can view patient assessment data, add treatment opinions, and retrieve historical reports. Management side: Can update the quantitative rule base, adjust weight parameters, and maintain system configuration, but has no permission to modify user's original data.

[0036] Specifically, the data storage and interaction unit adopts a distributed storage architecture, divided into local storage and cloud backup. Local storage is used to store recent (within 3 months) raw data, processed data, quantitative results, and evaluation reports, while cloud storage is used for long-term backup (can be stored for 3-5 years) to ensure data is not lost. The AES-256 encryption algorithm is used to anonymize and encrypt user personal information, health data, and evaluation reports. The anonymization process employs a "hiding key information" mode (e.g., hiding the last 6 digits of the ID number, the middle 4 digits of the mobile phone number, and the middle character of the name). A three-level hierarchical permission management mechanism is set up, with user-level permissions only allowing viewing and exporting. The user's own health data and assessment reports cannot be modified. Healthcare professionals with access can view the health data and assessment reports of the users they are responsible for, and can add healthcare opinions, but cannot modify the original data. Administrators with access can view all user data, adjust system parameters, and update the quantitative rule base, but cannot arbitrarily modify the user's original data. Interactive functions support access from three terminals: Web, APP, and WeChat mini-program. Users can query historical data and export assessment reports through these terminals. The system can push generated assessment reports and risk warning information to user terminals in real time, and also supports online interaction between users and healthcare professionals (such as consulting on health issues and providing health status feedback). Distributed storage combined with cloud backup ensures data security and traceability, preventing data loss. AES-256 encryption and a tiered access control mechanism effectively protect user privacy and prevent the leakage of health data and personal information, complying with the relevant requirements of the Personal Information Protection Law and the Medical Data Security Guidelines. Multi-terminal interactive functions enhance the system's convenience; users can query and manage their own health data anytime, anywhere, and healthcare professionals can obtain user health information in real time, improving health management efficiency.

[0037] This application also includes a health intervention recommendation unit, which is communicatively connected to the comprehensive health assessment unit. It has a preset health intervention rule base, which is used to automatically match personalized health intervention plans based on the user's comprehensive health score, health level, abnormal indicators and risk warning information, combined with the diet, exercise, rest and medical treatment related rules in the intervention rule base. These plans include dietary recommendations, exercise plans, rest adjustment suggestions and medical treatment prompts.

[0038] Specifically, the health intervention recommendation unit communicates in real time with the comprehensive health assessment unit. It has a pre-set health intervention rule base, built upon medical standards, nutritional guidelines, and sports medicine standards. This base includes four main categories of intervention rules: diet, exercise, sleep patterns, and medical visits. These rules are stored categorized by health level, type of abnormal indicator, and health risk trend (e.g., dietary rules for hypertensive patients, exercise rules for diabetic patients). The unit receives the user's comprehensive health score, health level, abnormal indicators, and risk warning information from the comprehensive health assessment unit. Combining this with the user's age, gender, occupation, and dietary habits, it matches the corresponding intervention rules from the rule base and automatically generates personalized recommendations. The personalized health intervention plan includes dietary recommendations that specify food choices, daily intake, and cooking methods (e.g., for patients with hypertension, daily salt intake should not exceed 5g, and more blood pressure-lowering foods such as celery and spinach should be consumed). The exercise plan specifies exercise type, duration, and frequency (e.g., for sub-healthy individuals, exercise 3-5 times a week for 30 minutes each time, primarily aerobic exercise). The sleep schedule recommendations specify daily sleep time and regularity (e.g., it is recommended to go to bed before 11 PM and ensure 7-8 hours of sleep). The medical consultation prompts specify the relevant department, examination items, and consultation time (e.g., for users with abnormal blood sugar, it is recommended to consult an endocrinologist within one week to test fasting blood sugar and 2-hour postprandial blood sugar). This personalized health intervention plan is tailored to the user's specific health condition and personal characteristics, avoiding the lack of specificity in general intervention recommendations. The intervention plan is specific and actionable, allowing users to directly follow the plan and reducing the difficulty of health improvement. The medical consultation prompts guide users to seek medical attention promptly, preventing the deterioration of health risks and further enhancing the system's health management value.

[0039] In this embodiment, the intervention plan generated by the health intervention recommendation unit includes clear implementation standards: dietary recommendations specify daily intake and food taboos, exercise plans specify exercise type, duration and frequency, sleep adjustment recommendations limit sleep time and sleep duration, and medical consultation prompts specify the department to be consulted, examination items and follow-up visit cycle. The plan can be directly executed by the user.

[0040] In this embodiment, the complete system workflow is as follows: 1. Users complete identity authentication via the terminal, the system initiates the data acquisition unit, and retrieves built-in data and synchronized data from external devices; 2. Raw data is input into the data preprocessing unit, where it is cleaned, deduplicated, outlier removed, missing value supplemented, and standardized; 3. Standardized data is input into the multi-dimensional indicator quantification unit, which generates quantitative scores for each dimension according to the quantification rule library and ranks the indicators by importance; 4. Quantified scores are input into the comprehensive health assessment unit, where an adaptive weighted algorithm is used to calculate a comprehensive score and classify health levels, while a risk prediction module outputs a risk warning; 5. Assessment results are synchronized to the report generation unit and the health intervention recommendation unit, generating a standardized assessment report and a personalized intervention plan, respectively; 6. Reports, intervention plans, and all data are synchronized to the data storage and interaction unit, where they are encrypted, archived with access permissions, and pushed to the user's terminal.

[0041] Taking a 35-year-old male user in a sub-healthy workplace as an example: The data acquisition unit collects three types of data: physiological signs (blood pressure 135 / 85 mmHg, heart rate 78 beats / min), lifestyle (exercise once a week, staying up late 3 times a week), and clinical examination (blood glucose 5.6 mmol / L). The preprocessing unit removes one abnormal heart rate data point, uses linear interpolation to supplement one missing motion data point, and converts it into standardized data in the [0,100] interval; Multi-dimensional indicator quantitative unit output: physiological signs 82 points, lifestyle 55 points, clinical examination 90 points. After ranking the importance of the indicators, lifestyle is the core influencing indicator. The comprehensive health assessment unit, calculated based on the weight of sub-healthy individuals (lifestyle weight 35%), yielded a comprehensive score of 72, indicating a general health level. The risk prediction module indicated an "increased risk of hypertension in the next 6 months." The report generation unit outputs visual chart reports, and the health intervention recommendation unit matches personalized plans such as "3 aerobic exercises per week and going to bed before 11 pm".

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional health indicator quantitative assessment and report generation system, characterized in that, It includes a data acquisition unit, a data preprocessing unit, a multi-dimensional indicator quantification unit, a comprehensive health assessment unit, a report generation unit, and a data storage and interaction unit that are connected in sequence via communication. The data acquisition unit is used to collect multi-dimensional health-related raw data of users, which includes at least three of the following: physiological signs data, lifestyle data, clinical examination data, and psychological state data. The data preprocessing unit is used to clean, deduplicate, remove outliers, and standardize the collected raw data to obtain standardized data that can be used for quantitative processing. The multi-dimensional indicator quantification unit is pre-set with an indicator quantification rule library based on medical standards. The quantification rule library contains quantification models corresponding to different dimensions of health indicators, which are used to transform standardized data into comparable and calculable quantitative scores, and complete the independent quantification of each dimension of health indicators. The comprehensive health assessment unit is equipped with an adaptive weighted assessment algorithm. The adaptive weighted assessment algorithm is based on a preset benchmark weight and automatically adjusts the weight coefficients of each dimension indicator in combination with the user's health status. It is used to calculate the user's comprehensive health score based on the quantitative scores of each dimension, classify health levels, and identify abnormal indicators and potential health risks. The report generation unit is used to automatically generate a standardized and structured health assessment report based on the comprehensive health assessment results, quantitative details of each dimension, abnormal indicator analysis, and health intervention suggestions, according to a preset template. The data storage and interaction unit is used to store all raw data, processed data, quantification results, and evaluation reports, and provides data query, report export, permission management, and external terminal interaction functions.

2. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The data acquisition unit includes a built-in acquisition module and an external interface module; the built-in acquisition module is used to directly collect the user's basic physiological data; the external interface module supports interface with wearable devices, medical testing instruments, electronic health record systems, and health management platforms to achieve automatic synchronization and real-time collection of health data from multiple channels.

3. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The outlier removal in the data preprocessing unit adopts a combination of the 3σ principle and manual verification. The original data that exceeds the normal range is marked, reviewed, and removed after being confirmed as invalid. At the same time, missing data is supplemented by linear interpolation to ensure data integrity.

4. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The quantitative rule base of the multi-dimensional indicator quantification unit supports dynamic updates. Based on the latest medical guidelines, clinical data and population characteristics, the quantification thresholds, scoring standards and quantification models of each dimension indicator can be adjusted through the back-end management terminal to adapt to the differentiated quantification needs of different age groups, genders and occupations.

5. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The adaptive weighted evaluation algorithm of the comprehensive health assessment unit can automatically adjust the weight coefficients of each dimension indicator according to the user's health status. The clinical examination data of people with chronic diseases has a higher weight than that of the general population, and the lifestyle data of people in a sub-healthy state has a higher weight than that of the general population.

6. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The comprehensive health assessment unit also includes a risk prediction module. Based on historical health data, current quantitative results, and clinical big data, the risk prediction module uses a random forest machine learning algorithm to predict the user's health risk trend over a future period and outputs targeted risk warning information.

7. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The report generation unit includes a template customization module and a report optimization module. The template customization module allows users or administrators to customize the content modules, layout format, and output format of the report. The report optimization module is used to perform syntax verification and logical organization on the generated report to ensure that the report content is accurate, well-organized, and easy to understand.

8. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The data storage and interaction unit employs encrypted storage technology to de-identify and encrypt user personal identity information, health data, and assessment reports. It also sets up a hierarchical access control mechanism, allowing only authorized users, medical staff, and system administrators to view and modify the corresponding data according to their permissions, thus ensuring data security and privacy.

9. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, It also includes a health intervention recommendation unit, which is communicatively connected to the comprehensive health assessment unit. It has a preset health intervention rule base, which is used to automatically match personalized health intervention plans based on the user's comprehensive health score, health level, abnormal indicators and risk warning information, combined with the diet, exercise, rest and medical treatment related rules in the intervention rule base. These plans include dietary suggestions, exercise plans, rest adjustment suggestions and medical treatment prompts.

10. The multi-dimensional health indicator quantitative assessment and report generation system according to claim 1, characterized in that, The multi-dimensional indicator quantification unit also includes an indicator importance ranking module, which is used to rank all quantitative indicators based on the degree of influence of each dimension indicator on the comprehensive health score, and highlight the core impact indicators in the evaluation report to provide key directions for users to improve their health.

Images