Skin disease health evaluation system integrating multiple clinical evaluation tools

Abstract

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

Description

Technical Field

[0001] The present invention relates to the technical field of skin disease health assessment, and in particular to a skin disease health assessment system integrating multiple clinical assessment tools. Background Art

[0002] Skin diseases are a common chronic disease with a large number of patients and complex and diverse causes, including genetic, immune, environmental, and lifestyle factors. With the improvement of living standards and the intensification of environmental changes, the incidence of skin diseases has increased year by year, which has had a great impact on patients' physical health and quality of life. Traditional treatments for skin diseases mainly rely on doctors' clinical experience and patients' feedback, but due to the complexity of skin diseases and individual differences, a single treatment method is often difficult to meet the personalized needs of each patient. The effectiveness of current treatments is often limited by insufficient patient information, lack of targeted treatment plans, and untimely monitoring and adjustments during treatment.

[0003] Existing skin disease health assessment and treatment systems usually rely on static, single data sources, such as symptom scores, diagnostic results, etc., and fail to effectively integrate data from different dimensions, resulting in treatment plans that lack pertinence and timeliness. Traditional diagnosis and treatment methods often rely on patients' self-reported symptoms and doctors' experience and judgment, lack scientific data support, and are difficult to make real-time adjustments to each patient's specific condition. Although some current systems have added physiological monitoring and symptom assessment modules, most of these modules exist in isolation, lack effective data interaction and information feedback mechanisms, and are difficult to achieve comprehensive and continuous health management. Summary of the invention

[0004] Based on the above objectives, the present invention provides a skin disease health assessment system integrating multiple clinical assessment tools.

[0005] A skin disease health assessment system integrating multiple clinical evaluation tools, including a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module, and a feedback and optimization module, wherein;

[0006] The patient information management and dynamic risk assessment module is used to collect the patient's basic information, medical history and allergy history, dynamically assess the patient's risk of developing skin diseases by combining machine learning algorithms, generate personalized health management recommendations, and update the patient's files in real time;

[0007] The skin microbiome analysis module collects skin samples from patients, applies high-throughput genome sequencing technology to analyze the composition of the microbiome, evaluates the association between the microbiome and skin diseases, and provides targeted treatment recommendations;

[0008] The symptom quantification and assessment module quantitatively assesses the patient's skin symptoms through standardized questionnaires and image analysis technology to generate a comprehensive score;

[0009] The physiological index monitoring module monitors the physiological index data of the patient's skin in real time, including temperature, humidity and microcirculation status, and analyzes the physiological indexes of the patient's skin to provide multi-dimensional health information;

[0010] The environmental adaptability monitoring module is used to monitor the patient's living environment parameters, analyze the impact of climate change, pollutant concentrations and indoor and outdoor environmental parameters on skin health, and provide corresponding suggestions;

[0011] The intelligent decision support module generates personalized health assessment reports and treatment plans based on the data of each module using deep neural network algorithms and reinforcement learning algorithms to assist doctors in making decisions;

[0012] The feedback and optimization module collects patients' treatment feedback and clinical data, optimizes subsequent evaluation and intervention strategies through dynamic analysis, and forms a closed-loop system of continuous improvement.

[0013] Optionally, the patient information management and dynamic risk assessment module specifically includes:

[0014] Collect the patient's basic information, including name, age, gender, contact information and address, and establish a patient file;

[0015] Record the patient's medical history, including previous skin disease history, family medical history and allergy history, to ensure that the information is comprehensive;

[0016] Construct and use support vector machine models to preprocess and extract features from collected patient information;

[0017] Based on the support vector machine model, dynamic risk assessment is carried out, and based on the risk assessment results, personalized health management suggestions are generated, including lifestyle adjustments, regular check-ups and preventive measures;

[0018] Update patient files in real time to ensure the latest status of patient information and risk assessment results.

[0019] Optionally, the skin microbiome analysis module includes:

[0020] Collect skin samples from patients, including bacteria, fungi, and viruses on the skin surface, to ensure the representativeness and integrity of the samples;

[0021] The collected skin samples were processed using aseptic techniques and standardized operating procedures to avoid sample contamination and ensure the accuracy of subsequent analysis;

[0022] High-throughput genome sequencing technology was used to sequence the processed skin samples to obtain the whole genome information of the microbiome, including the types of microorganisms and their abundance;

[0023] Bioinformatics tools and software are used to analyze sequencing data, extract composition information of microbial communities, and generate classification and abundance spectra of microbiomes.

[0024] Optionally, the skin microbiome analysis module further includes:

[0025] To evaluate the association between the microbiome and skin diseases, use principal component analysis to reduce the dimensionality of the microbiome, identify the main components associated with skin disease status, and explore the relationship between changes in specific microbial communities and the onset of skin diseases;

[0026] Based on the analysis results, provide targeted treatment recommendations, including treatment plans, lifestyle adjustments and nutritional advice to improve the patient's skin health;

[0027] Patients’ microbiome analysis reports are updated in real time and integrated with their health records.

[0028] Optionally, the symptom quantification and assessment module includes:

[0029] A standardized questionnaire was designed, which covered the patients' skin symptoms, severity, and influencing factors, and the Likert scale was used to quantitatively score the symptoms;

[0030] Provide standardized questionnaires to patients, collect self-assessment information from patients, organize and summarize the collected questionnaire data, calculate the questionnaire score for each patient, and generate a personalized symptom assessment report;

[0031] Use image analysis technology to quantitatively evaluate patients' skin symptoms and generate corresponding image analysis scores;

[0032] The questionnaire scores are integrated with the image analysis scores to form a comprehensive score in order to more comprehensively evaluate the patient's skin symptoms and disease changes. The comprehensive score is updated regularly to record the trend of disease changes.

[0033] Optionally, the physiological indicator monitoring module includes:

[0034] Real-time monitoring of the patient's skin physiological index data, including temperature, humidity and microcirculation status, and real-time analysis of the collected physiological index data;

[0035] Generates a personalized physiological indicator report based on the analysis results. The report includes the status, fluctuation trend and difference from the normal range of temperature, humidity and microcirculation, helping doctors evaluate skin health status;

[0036] Based on the analysis results of the patient's physiological indicators, we provide multi-dimensional health advice, including personalized advice on skin care, living environment optimization, diet and daily habits to promote skin health;

[0037] Physiological indicator data is updated regularly and associated with the patient's treatment process and symptom assessment results to optimize health management plans and provide continuous health feedback.

[0038] Optionally, the environmental adaptability monitoring module includes:

[0039] The environmental adaptability monitoring module monitors the patient's living environment parameters, including climate change, pollutant concentrations, and indoor and outdoor environmental parameters, and performs real-time analysis on the collected patient living environment parameters;

[0040] Generate personalized environmental health assessment reports based on real-time analysis results;

[0041] Regularly update environmental health assessment reports and provide real-time feedback on the impact of environmental changes on skin health, helping patients adjust their lifestyles according to environmental changes and optimize skin care plans;

[0042] Based on the patient's skin symptoms and physiological indicator data analysis results, we provide health advice that is adaptable to the environment, ensure that the treatment plan matches the environmental changes, and improve the treatment effect and skin health.

[0043] Optionally, the intelligent decision support module includes:

[0044] Data from the patient information management and dynamic risk assessment module, skin microbiome analysis module, symptom quantification and evaluation module, physiological indicator monitoring module and environmental adaptability monitoring module are collected, preprocessed and standardized, and based on the processed data, a deep neural network algorithm is applied to build a patient health assessment model to generate a personalized health assessment report.

[0045] Optionally, the intelligent decision support module further includes:

[0046] Based on the generated health assessment report, a reinforcement learning algorithm is used to generate a personalized treatment plan. By displaying the health assessment report and personalized treatment plan, doctors can be helped to develop an accurate treatment plan.

[0047] Optionally, the feedback and optimization module includes:

[0048] Collect patients' treatment feedback and clinical data, and dynamically analyze the collected treatment feedback and clinical data, optimize assessment and intervention strategies based on the analysis results, and update patients' files in real time.

[0049] Beneficial effects of the present invention:

[0050] The present invention realizes dynamic risk assessment based on the patient's basic information, medical history and allergy history through the patient information management and dynamic risk assessment module. Combined with the support vector machine model, the module can calculate the patient's risk of skin disease onset in real time, generate personalized health management suggestions, and dynamically update the patient's profile. This not only provides doctors with a scientific basis for evaluation, but also enables patients to obtain tailored treatment suggestions, thereby improving the accuracy of treatment and patient satisfaction.

[0051] The present invention uses high-throughput genome sequencing technology to analyze the composition of the patient's skin microbiome through the skin microbiome analysis module, evaluates its association with skin diseases, and thus provides patients with scientific microbiome intervention strategies. At the same time, the symptom quantification and evaluation module quantitatively evaluates the patient's skin symptoms through standardized questionnaires and image analysis technology, and provides accurate symptom scores. These multi-dimensional data (including microbiome, skin symptoms, health status, etc.) can comprehensively track the changes and progression of skin diseases, and monitor the physiological indicators of the patient's skin (such as temperature, humidity, microcirculation, etc.) in real time through the physiological indicator monitoring module, and generate a health assessment report. These steps can comprehensively evaluate the patient's skin health status, and make timely adjustments to the intervention strategy based on real-time data to ensure the optimization of the treatment effect.

[0052] In the present invention, the patient's treatment feedback and clinical data are collected and analyzed in real time, and the treatment plan is optimized through variance analysis. Through the health assessment model established based on the deep neural network algorithm, the assessment results can be continuously updated, and the plan can be dynamically adjusted according to the patient's treatment response. The analysis and optimization of each treatment feedback not only helps medical staff make more accurate decisions, but also forms a closed-loop system through the update of the patient's health record, so that the treatment plan can adapt to the patient's health changes, provide long-term, personalized health management services, further improve the patient's quality of life and reduce the recurrence of skin diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of the skin microbiome analysis module process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0057] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0058] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0059] like Figure 1-Figure 2 As shown, a skin disease health assessment system integrating multiple clinical evaluation tools includes a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module, wherein;

[0060] The patient information management and dynamic risk assessment module is used to collect the patient's basic information, medical history and allergy history, dynamically assess the patient's risk of developing skin diseases by combining machine learning algorithms, generate personalized health management recommendations, and update the patient's files in real time;

[0061] The skin microbiome analysis module collects skin samples from patients, applies high-throughput genome sequencing technology to analyze the composition of the microbiome, evaluates the association between the microbiome and skin diseases, and provides targeted treatment recommendations;

[0062] The symptom quantification and assessment module quantitatively assesses the patient's skin symptoms through standardized questionnaires and image analysis technology, and generates a comprehensive score to facilitate tracking of disease changes;

[0063] The physiological index monitoring module monitors the physiological index data of the patient's skin in real time, including temperature, humidity and microcirculation status, and analyzes the physiological indexes of the patient's skin to provide multi-dimensional health information;

[0064] The environmental adaptability monitoring module is used to monitor the patient's living environment parameters, analyze the impact of climate change, pollutant concentrations and indoor and outdoor environmental parameters on skin health, and provide corresponding suggestions;

[0065] The intelligent decision support module generates personalized health assessment reports and treatment plans based on the data of each module using deep neural network algorithms and reinforcement learning algorithms to assist doctors in making decisions;

[0066] The feedback and optimization module collects patients' treatment feedback and clinical data, optimizes subsequent evaluation and intervention strategies through dynamic analysis, and forms a closed-loop system of continuous improvement.

[0067] Optionally, the patient information management and dynamic risk assessment module specifically includes:

[0068] Collect the patient's basic information, including name, age, gender, contact information and address, and establish a patient file;

[0069] Record the patient's medical history, including previous skin disease history, family medical history and allergy history, to ensure that the information is comprehensive;

[0070] Construct and use a support vector machine model to preprocess and extract features from the collected patient information for subsequent analysis. The specific steps include:

[0071] (1) Normalize the collected patient information to ensure that different features are at the same level;

[0072] (2) Select key features, such as patient age, allergy history, and medical history information, and perform feature selection through training data sets to improve the effectiveness of the model;

[0073] (3) Train the support vector machine model, evaluate the accuracy of the model using cross-validation methods, and adjust hyperparameters to optimize model performance;

[0074] Based on the support vector machine model, dynamic risk assessment is carried out, including:

[0075] (1) Receive new patient feature data, including the patient’s basic information, medical history, allergy history, and real-time monitored physiological indicators, and form a new feature vector X new ;

[0076] (2) Perform the same preprocessing steps on the received new feature data as the training data to ensure that the new data is consistent with the model input format, including normalization and missing value filling;

[0077] (3) The new feature vector X after preprocessing new Input it into the trained support vector machine model and use the model's decision function to calculate and obtain the skin disease onset risk score R:

[0078] R=SVM(X new );

[0079] Among them, the model makes predictions based on the support vectors and decision boundaries learned during training;

[0080] (4) comparing the calculated risk score R with the preset risk threshold to determine the patient's skin disease onset risk level (e.g., low risk, medium risk, high risk);

[0081] Generate personalized health management recommendations based on risk assessment results, including lifestyle adjustments, regular checkups, and preventive measures;

[0082] Update patient files in real time to ensure the latest status of patient information and risk assessment results to support subsequent health management;

[0083] The patient information management and dynamic risk assessment module comprehensively collects the patient's basic information and medical history, combines it with the support vector machine algorithm to achieve dynamic assessment, effectively generates personalized health management recommendations, and ensures real-time updating of patient files, thereby improving the scientificity and effectiveness of skin disease prevention and management.

[0084] Optionally, the skin microbiome analysis module includes:

[0085] Collect skin samples from patients, including bacteria, fungi, and viruses on the skin surface, to ensure the representativeness and integrity of the samples;

[0086] The collected skin samples were processed using aseptic techniques and standardized operating procedures to avoid sample contamination and ensure the accuracy of subsequent analysis;

[0087] High-throughput genome sequencing technology is used to sequence the processed skin samples to obtain the whole genome information of the microbiome, including the types of microorganisms and their abundance. The specific steps include:

[0088] (1) Extract DNA from skin samples using appropriate kits (e.g., DNeasy Blood & Tissue Kit) and standardized procedures to ensure DNA integrity and purity;

[0089] (2) Perform quality checks on the extracted DNA, including concentration measurement using agarose gel electrophoresis and a nanotiter, to ensure the validity of sequencing;

[0090] (3) Constructing a library using the Illumina library construction method, the specific steps include:

[0091] (3.1) Fragment the extracted DNA using an ultrasonic disruptor to fragment the DNA into fragments of appropriate size (usually 200-600 bp);

[0092] (3.2) Connect adapters to add specific adapter sequences to each DNA fragment for subsequent sequencing;

[0093] (3.3) performing PCR amplification and using appropriate primers to amplify the DNA fragments with adapters to increase the amount of the library and ensure the complexity of the library;

[0094] (3.4) Loading the constructed library onto the Illumina high-throughput sequencing platform for sequencing to generate raw sequencing data;

[0095] Bioinformatics tools and software are used to analyze sequencing data, extract composition information of microbial communities, and generate classification and abundance profiles of microbial groups, including:

[0096] (1) Perform quality control on raw sequencing data, use FastQC software to assess data quality, and remove low-quality sequences and adapter sequences to ensure data reliability;

[0097] (2) The processed sequences were spliced ​​and de-redundant, and high-quality sequences were generated using the splicing software PEAR to ensure the integrity and accuracy of the data;

[0098] (3) The spliced ​​sequences were compared with the reference database Greengenes, and the species classification was performed using the classification tool QiiME to determine the composition of the microbiome and obtain the classification information of each microbial species;

[0099] (4) Calculate the abundance of each microbial species and generate an abundance spectrum to show the relative abundance of each microbial species in the sample for subsequent analysis.

[0100] Optionally, the skin microbiome analysis module further includes:

[0101] To evaluate the association between the microbiome and skin diseases, principal component analysis was used to reduce the dimensionality of the microbiome, identify the main components associated with skin disease status, and explore the relationship between changes in specific microbial communities and the onset of skin diseases, including:

[0102] (1) Organize the abundance data into a data matrix, where rows represent samples, columns represent microbial species, and data values ​​represent abundance;

[0103] (2) Standardize the data matrix to ensure that different features are at the same level, using Z-score normalization or Min-Max normalization method;

[0104] (3) Apply principal component analysis and use statistical software (such as the scikit-learn library in R or Python) to perform dimensionality reduction analysis and extract the main components;

[0105] (4) visualize the principal component analysis results, display the distribution of samples in the principal component space through scatter plots, and identify the microbial community characteristics associated with skin disease status;

[0106] (5) Based on the results of principal component analysis and combined with clinical data, explore the relationship between microbiome composition and skin disease onset and conduct correlation analysis;

[0107] Based on the analysis results, provide targeted treatment recommendations, including treatment plans, lifestyle adjustments and nutritional advice to improve the patient's skin health;

[0108] Update the patient's microbiome analysis report in real time and integrate it with the patient's health record to support subsequent health management and treatment decisions;

[0109] The skin microbiome analysis module systematically collects and analyzes patient skin samples, uses high-throughput genome sequencing technology to achieve in-depth analysis of the microbiome, and uses principal component analysis to evaluate the correlation between the microbiome and skin diseases, thereby providing patients with personalized treatment recommendations and promoting the scientificity and effectiveness of skin health management.

[0110] Optionally, the symptom quantification and assessment module includes:

[0111] Design a standardized questionnaire that covers the patient's skin symptoms, severity, and influencing factors, and uses a Likert scale (e.g., 0-10 points) to quantitatively score the symptoms to ensure the validity and reliability of the questionnaire;

[0112] Provide patients with standardized questionnaires, collect self-assessment information from patients, ensure anonymity and privacy protection of questionnaire filling, organize and summarize the collected questionnaire data, calculate the questionnaire score for each patient, and generate a personalized symptom assessment report;

[0113] Image analysis technology is used to quantitatively evaluate the patient's skin symptoms and generate corresponding image analysis scores. The specific steps include:

[0114] (1) Use a high-resolution camera to capture images of the patient's skin symptoms, ensuring consistent image quality and shooting angle;

[0115] (2) Use image processing software (such as ImageJ or OpenCV) to process the images, including image preprocessing, feature extraction, and symptom area segmentation;

[0116] (3) Quantitatively analyze skin symptoms in images, extract key features (such as area, color change, and shape features), and generate corresponding image analysis scores;

[0117] Integrate the questionnaire score with the image analysis score to form a comprehensive score to more comprehensively evaluate the patient's skin symptoms and disease changes. Regularly update the comprehensive score and record the trend of disease changes to assist in subsequent treatment decisions and health management.

[0118] The symptom quantification and assessment module systematically and quantitatively assesses patients' skin symptoms and generates a comprehensive score through standardized questionnaires and image analysis technology. This method provides a scientific basis for disease monitoring, can effectively track changes in patients' skin health, and provide support for the formulation of personalized treatment plans.

[0119] Optionally, the physiological indicator monitoring module includes:

[0120] Real-time monitoring of the patient's skin physiological indicators, including temperature, humidity and microcirculation status. The specific steps include:

[0121] (1) Use an infrared temperature sensor or a patch temperature sensor to detect the temperature of the patient's skin surface in real time and ensure the accuracy and stability of the sensor;

[0122] (2) Use a capacitive humidity sensor to detect humidity changes on the patient's skin surface in real time to ensure accurate measurement of humidity values;

[0123] (3) Monitor the microcirculation status of the patient's skin by laser Doppler flow meter or near-infrared spectrometer to evaluate changes in blood flow on the skin surface;

[0124] Real-time analysis of the collected physiological index data is performed. The specific steps include:

[0125] (1) Analyze temperature data fluctuations, identify abnormal temperature trends or patterns, and compare with standard reference values ​​to analyze skin health status;

[0126] (2) Analyze humidity data and assess the health of the patient's skin hydration and skin barrier function by monitoring changes in skin humidity;

[0127] (3) Analyze microcirculation data to assess the patient's skin blood circulation, especially blood flow velocity and microvascular dilation, and infer the skin's oxygen supply and nutritional status;

[0128] Generates a personalized physiological indicator report based on the analysis results. The report includes the status, fluctuation trend and difference from the normal range of temperature, humidity and microcirculation, helping doctors evaluate skin health status;

[0129] Based on the analysis results of the patient's physiological indicators, we provide multi-dimensional health advice, including personalized advice on skin care, living environment optimization, diet and daily habits to promote skin health;

[0130] Regularly update physiological indicator data and associate it with the patient's treatment process and symptom assessment results to optimize health management plans and provide continuous health feedback;

[0131] The physiological indicator monitoring module comprehensively collects and analyzes the patient's skin physiological data by real-time monitoring of skin temperature, humidity and microcirculation status, and generates personalized health reports and recommendations. This module helps provide data support for the early detection, treatment adjustment and health management of skin diseases, thereby improving the patient's skin health management effect.

[0132] Optionally, the environmental adaptability monitoring module includes:

[0133] The environmental adaptability monitoring module monitors the patient's living environment parameters, including climate change, pollutant concentrations, and indoor and outdoor environmental parameters. The specific steps include:

[0134] (1) Use meteorological sensors to collect local climate data in real time, including climate parameters such as temperature, humidity, air pressure, wind speed and precipitation, to ensure the timeliness and accuracy of environmental data;

[0135] (2) Use air quality monitoring sensors (such as PM2.5, PM10, CO2, and NOx sensors) to measure the concentration of pollutants in the air, especially pollutants that may affect skin health (such as particulate matter, volatile organic compounds, etc.);

[0136] (3) Use indoor environmental sensors (such as indoor temperature and humidity sensors, carbon dioxide concentration sensors, light intensity sensors, etc.) to monitor the indoor environmental parameters of the patient to ensure that the patient's overall living environment is covered;

[0137] Real-time analysis of the collected patient living environment parameters is performed. The specific steps include:

[0138] (1) To analyze the correlation between climate parameters and patients’ skin health status and to identify the impact of climate change on skin health, especially the potential role of seasonal factors in the onset of skin diseases;

[0139] (2) Analyze pollutant concentrations to assess the impact of air pollution on skin health, especially the irritation and oxidative damage of high concentrations of pollutants (such as PM2.5 and ozone) on the skin;

[0140] (3) Analyze the impact of indoor environmental parameters on skin health, such as temperature and humidity discomfort, light intensity, and insufficient air circulation, and speculate on the impact of these factors on skin barrier function, humidity balance, etc.;

[0141] Generate a personalized environmental health assessment report based on real-time analysis results, including:

[0142] (1) Comprehensively analyze the impact of environmental factors and point out the potential risks of climate change, pollutant concentrations, and indoor and outdoor environmental conditions to patients' skin health;

[0143] (2) Based on the analysis results, provide patients with personalized suggestions for optimizing their living environment and provide specific prevention and mitigation measures for adverse factors in the environment (such as air pollution and high humidity);

[0144] Regularly update environmental health assessment reports and provide real-time feedback on the impact of environmental changes on skin health, helping patients adjust their lifestyles according to environmental changes and optimize skin care plans;

[0145] Based on the patient's skin symptoms and physiological indicator data analysis results, we provide health advice that is adaptable to the environment, ensure that the treatment plan matches the environmental changes, and improve the treatment effect and skin health.

[0146] Optionally, the intelligent decision support module includes:

[0147] Collect data from the patient information management and dynamic risk assessment module, skin microbiome analysis module, symptom quantification and assessment module, physiological indicator monitoring module, and environmental adaptability monitoring module to ensure the integrity and accuracy of the data, including:

[0148] (1) Collect the patient's basic information, medical history, and allergy history, as well as skin disease risk data assessed by machine learning algorithms;

[0149] (2) Collect data on the composition of the skin microbiome and analyze its association with skin diseases;

[0150] (3) collect questionnaire scores and image analysis results (symptom quantitative assessment results);

[0151] (4) Collect real-time monitoring data of physiological indicators (such as temperature, humidity, and microcirculatory status);

[0152] (5) Collect data on climate change, pollutant concentrations, and indoor and outdoor environmental parameters provided by the environmental adaptability monitoring module;

[0153] Preprocessing and standardization are performed to ensure the comparability and consistency of the data. The processing steps include:

[0154] (1) Time synchronization of data from different sources to ensure that data from each module can be compared and comprehensively analyzed at the same time point;

[0155] (2) Eliminate or correct outliers to ensure data integrity and quality;

[0156] Based on the processed data, a deep neural network algorithm is used to build a patient health assessment model and generate a personalized health assessment report. The specific steps include:

[0157] (1) The deep neural network algorithm is selected as the machine learning algorithm. Through the connection of multiple hidden layers of neurons, the deep features of the data of each module can be automatically extracted to adapt to the complex nonlinear relationship in skin disease health assessment. The deep neural network algorithm can effectively integrate the information provided by different modules (such as symptom quantification, microbiome analysis, physiological indicators, etc.) to conduct a comprehensive assessment of skin health status;

[0158] (1.1) Design the network structure according to the type of input data, including the input layer (receives various types of data), several hidden layers (automatically learns the nonlinear relationship between data), and the output layer (outputs health assessment results and treatment recommendations);

[0159] (1.2) Through multiple iterations of training (using the back propagation algorithm to optimize model parameters), the model's prediction accuracy for patients' skin health status, disease progression, and treatment response is gradually improved;

[0160] (2) Use the trained patient health assessment model to input new patient data, assess the risk of skin disease onset, possible causes, current skin health status, and future disease development trends, and generate a personalized health assessment report.

[0161] Optionally, the intelligent decision support module further includes:

[0162] Based on the generated health assessment report, a reinforcement learning algorithm is used to generate a personalized treatment plan. The specific steps include:

[0163] (1) Reinforcement learning was selected as the optimization algorithm, which is capable of handling multivariable and multi-objective optimization problems and adjusting treatment plans according to the patient's dynamic health status. The treatment strategy was optimized by establishing a decision-making model based on reinforcement learning.

[0164] (1.1) Define the state space, which includes the patient's skin health status, microbiome data, symptom quantitative scores, physiological indicators, environmental adaptability monitoring data, etc. Each state represents the patient's complete health information at a certain point in time;

[0165] (1.2) Define the action space, which includes available treatment measures, such as drug type, dosage, treatment frequency, skin care recommendations, lifestyle adjustments, etc. Each action represents a treatment decision;

[0166] (1.3) Design a reward function that guides the optimization process by evaluating the treatment effect (such as improvement of skin symptoms, optimization of physiological indicators, etc.). Long-term effective treatment strategies will receive higher rewards.

[0167] (1.4) Through iterative learning and environmental feedback, the reinforcement learning algorithm dynamically optimizes the strategy when adjusting the treatment plan at each step, gradually improving the treatment effect;

[0168] (2) Generate a personalized treatment plan that includes optimized drug therapy, lifestyle intervention, nursing advice, etc. based on the patient's skin condition, microbiome data, physiological indicators, environmental factors, etc., and provides a timetable and adjustment strategy for treatment implementation;

[0169] (2.1) Generate recommended treatment plans based on reinforcement learning models, covering drug use (type, dosage), treatment frequency, nursing methods, etc.;

[0170] (2.2) As patient feedback and treatment outcomes change, the model will continuously update and adjust strategies to ensure that the treatment plan always matches the patient’s dynamic health status;

[0171] By displaying health assessment reports and personalized treatment plans, we help doctors develop accurate treatment plans. The specific steps include:

[0172] (1) Present the assessment report and treatment plan to the doctor through a visual interface, ensuring that the doctor can quickly understand the patient's health status and treatment plan;

[0173] (2) Provide dynamic adjustment recommendations based on patients’ treatment feedback and clinical monitoring data, and update health assessment reports and treatment plans in real time to support doctors in making flexible decisions during the treatment process.

[0174] Optionally, the feedback and optimization module includes:

[0175] Collect patient treatment feedback and clinical data. Specific steps include:

[0176] (1) Collecting feedback information provided by patients during the treatment process through the user interface, including self-reported symptom improvement, discomfort and side effects during treatment;

[0177] (2) Collect relevant data from clinicians on patients’ treatment progress, treatment effects, medication adjustments, etc.;

[0178] (3) Update treatment feedback based on patients’ real-time data (e.g., physiological monitoring, symptom assessment, etc.);

[0179] The collected treatment feedback and clinical data are dynamically analyzed. The specific steps include:

[0180] (1) Compare patients’ treatment feedback with historical data to assess trends in treatment effectiveness;

[0181] (2) Use analysis of variance to process treatment feedback, analyze the differences in patient responses to different treatment options (such as drug type, treatment method, treatment course length, etc.), evaluate which factors have a significant impact on treatment outcomes, and then optimize treatment strategies;

[0182] Through analysis, potential problems in treatment can be identified and whether treatment strategies need to be adjusted;

[0183] Optimize assessment and intervention strategies based on the analysis results. Specific steps include:

[0184] (1) Based on the constructed patient health assessment model, combined with treatment feedback and patient health status data, the health assessment results are dynamically updated;

[0185] (2) Adjust treatment plans, such as medication dosage, treatment frequency, or personalized care plans, based on updated health assessment results;

[0186] (3) Generate adjusted personalized treatment recommendations and notify doctors and patients through the system to guide the subsequent treatment process;

[0187] Update the patient's profile in real time. The specific steps include:

[0188] (1) Update the optimized treatment plan and health assessment report to the patient's electronic health record in real time;

[0189] (2) Update the patient’s long-term health tracking information based on new treatment data and provide a basis for subsequent treatment.

[0190] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0191] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A skin disease health assessment system integrating multiple clinical assessment tools, characterized in that: It includes patient information management and dynamic risk assessment module, skin microbiome analysis module, symptom quantification and assessment module, physiological indicator monitoring module, environmental adaptability monitoring module, intelligent decision support module and feedback and optimization module, among which; The patient information management and dynamic risk assessment module is used to collect the patient's basic information, medical history and allergy history, dynamically assess the patient's risk of developing skin diseases by combining machine learning algorithms, generate personalized health management recommendations, and update the patient's files in real time; The skin microbiome analysis module collects skin samples from patients, applies high-throughput genome sequencing technology to analyze the composition of the microbiome, evaluates the association between the microbiome and skin diseases, and provides targeted treatment recommendations; The symptom quantification and assessment module quantitatively assesses the patient's skin symptoms through standardized questionnaires and image analysis technology, and generates a comprehensive score to facilitate tracking of disease changes; The physiological index monitoring module monitors the physiological index data of the patient's skin in real time, including temperature, humidity and microcirculation status, and analyzes the physiological indexes of the patient's skin to provide multi-dimensional health information; The environmental adaptability monitoring module is used to monitor the patient's living environment parameters, analyze the impact of climate change, pollutant concentrations and indoor and outdoor environmental parameters on skin health, and provide corresponding suggestions; The intelligent decision support module generates personalized health assessment reports and treatment plans based on the data of each module using deep neural network algorithms and reinforcement learning algorithms to assist doctors in making decisions; The feedback and optimization module collects patients' treatment feedback and clinical data, optimizes subsequent evaluation and intervention strategies through dynamic analysis, and forms a closed-loop system of continuous improvement.

2. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 1, characterized in that: The patient information management and dynamic risk assessment module specifically includes: Collect the patient's basic information, including name, age, gender, contact information and address, record the patient's medical history, including previous skin disease history, family medical history and allergy history, and establish a patient file; Construct and use a support vector machine model to preprocess and extract features from collected patient information for subsequent analysis; Based on the support vector machine model, dynamic risk assessment is carried out, and based on the risk assessment results, personalized health management suggestions are generated, including lifestyle adjustments, regular check-ups and preventive measures; Update patient files in real time to ensure the latest status of patient information and risk assessment results.

3. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 2, characterized in that: The skin microbiome analysis module includes: Collect skin samples from patients, including samples of bacteria, fungi, and viruses on the skin surface; The collected skin samples were processed using aseptic techniques and standardized operating procedures to avoid sample contamination; High-throughput genome sequencing technology was used to sequence the processed skin samples to obtain the whole genome information of the microbiome, including the types of microorganisms and their abundance; Bioinformatics tools and software are used to analyze sequencing data, extract composition information of microbial communities, and generate classification and abundance spectra of microbiomes.

4. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 3, characterized in that: The skin microbiome analysis module also includes: To evaluate the association between the microbiome and skin diseases, use principal component analysis to reduce the dimensionality of the microbiome, identify the main components associated with skin disease status, and explore the relationship between changes in specific microbial communities and the onset of skin diseases; Based on the analysis results, provide targeted treatment recommendations, including treatment plans, lifestyle adjustments and nutritional advice; Patients’ microbiome analysis reports are updated in real time and integrated with their health records.

5. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 4, characterized in that: The symptom quantification and assessment module includes: A standardized questionnaire was designed, which covered the patients' skin symptoms, severity, and influencing factors, and the Likert scale was used to quantitatively score the symptoms; Provide standardized questionnaires to patients, collect self-assessment information from patients, organize and summarize the collected questionnaire data, calculate the questionnaire score for each patient, and generate a personalized symptom assessment report; Use image analysis technology to quantitatively evaluate patients' skin symptoms and generate corresponding image analysis scores; Integrate the questionnaire scores with the image analysis scores to form a comprehensive score, and update the comprehensive score regularly to record the trend of disease changes.

6. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 5, characterized in that: The physiological index monitoring module includes: Real-time monitoring of the patient's skin physiological index data, including temperature, humidity and microcirculation status, and real-time analysis of the collected physiological index data; Generates a personalized physiological indicator report based on the analysis results. The report includes the status, fluctuation trend and difference from the normal range of temperature, humidity and microcirculation, helping doctors evaluate skin health status; Provide multi-dimensional health advice based on the patient's physiological indicators, including skin care, living environment optimization, diet and daily habits; Physiological indicator data is updated regularly and associated with the patient's treatment process and symptom assessment results to optimize health management plans and provide continuous health feedback.

7. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 6, characterized in that: The environmental adaptability monitoring module includes: The environmental adaptability monitoring module monitors the patient's living environment parameters, including climate change, pollutant concentrations, and indoor and outdoor environmental parameters, and performs real-time analysis on the collected patient living environment parameters; Generate personalized environmental health assessment reports based on real-time analysis results; Regularly update environmental health assessment reports and provide real-time feedback on the impact of environmental changes on skin health, helping patients adjust their lifestyles according to environmental changes; Based on the patient's skin symptoms and physiological indicator data analysis results, health advice that is adaptable to the environment is provided.

8. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 7, characterized in that: The intelligent decision support module includes: Data from the patient information management and dynamic risk assessment module, skin microbiome analysis module, symptom quantification and evaluation module, physiological indicator monitoring module and environmental adaptability monitoring module are collected, preprocessed and standardized, and based on the processed data, a deep neural network algorithm is applied to build a patient health assessment model to generate a personalized health assessment report.

9. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 8, characterized in that: The intelligent decision support module also includes: Based on the generated health assessment report, a reinforcement learning algorithm is used to generate a personalized treatment plan, which helps doctors develop a treatment plan by displaying the health assessment report and personalized treatment plan.

10. A skin disease health assessment system integrating multiple clinical assessment tools according to claim 9, characterized in that: The feedback and optimization module includes: Collect patients' treatment feedback and clinical data, and dynamically analyze the collected treatment feedback and clinical data, optimize assessment and intervention strategies based on the analysis results, and update patients' files in real time.

Images