Intelligent clinical examination result interpretation system

Abstract

The invention relates to the technical field of medical data processing, in particular to an intelligent clinical examination result interpretation system which comprises a data acquisition module, a preliminary analysis module, a depth pattern recognition module, a disease prediction module, a personalized treatment module, a treatment effect monitoring module, an epigenetics analysis module and a knowledge integration and decision support module. According to the method, the accuracy and efficiency of preliminary diagnosis are improved through statistical analysis and time sequence analysis, the deep learning algorithm shows excellent performance in early lesion detection and complex disease pattern recognition, the gradient elevator provides a powerful tool in disease prediction, the disease development trend is predicted, patient conditions are considered in a personalized treatment scheme, and the diagnosis accuracy and efficiency are improved. According to the system, accurate and effective treatment is ensured, a real-time monitoring and data analysis method provides real-time feedback for doctors in treatment effect evaluation, flexible and timely adjustment is promoted, epigenetics analysis provides a new perspective for understanding a disease molecular mechanism, and a knowledge integration and decision support module improves clinical decision quality and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and particularly to an intelligent clinical test result interpretation system. Background Art

[0002] The technical field of medical data processing is a field that focuses on using computational methods and tools to analyze, interpret, and utilize medical data. This field encompasses a wide range of applications from basic data recording and management to complex data analysis and prediction models. Key technologies include data mining, pattern recognition, machine learning, artificial intelligence, big data analysis, and visualization techniques. These technologies enable medical professionals to extract valuable information from large amounts of clinical data, thereby helping to make more accurate decisions in disease diagnosis, treatment planning, disease prediction, and patient care. With the advancement of technology, medical data processing has become increasingly automated and intelligent, thus improving the efficiency and effectiveness of medical services.

[0003] Among them, the intelligent clinical test result interpretation system is an advanced system that combines artificial intelligence, data analysis, and machine learning technologies, aiming to automatically interpret and analyze clinical test results. The main purpose of this system is to relieve the burden on medical professionals, improve the accuracy and efficiency of diagnosis, and provide more personalized treatment plans for patients. By analyzing the test data of patients, this system can assist in diagnosing diseases, monitoring the development of the condition, predicting the treatment effect, and even detecting early signs of diseases. To achieve these effects, the intelligent clinical test result interpretation system usually utilizes deep learning and neural networks to identify and interpret patterns and associations in the test data. These algorithms can learn from a large amount of historical data, and with the passage of time and the accumulation of data, their accuracy and reliability will continue to improve. In addition, the system also integrates advanced data visualization tools to enable doctors to intuitively understand and interpret the analysis results. Through these means, the intelligent clinical test result interpretation system can provide fast, accurate, and comprehensive data analysis, helping to improve the overall medical quality and patient care level.

[0004] Traditional methods for interpreting clinical test results have some deficiencies. Preliminary analysis usually relies on basic statistical methods and lacks the ability to deeply analyze potential complex disease patterns. Traditional methods are usually not precise enough in the early identification and prediction of diseases, resulting in treatment plans that are not personalized or timely enough. In the monitoring and evaluation of treatment effects, traditional methods often lag behind and cannot provide real-time feedback, affecting the timely adjustment of treatment. Traditional methods rarely consider the genetic background and epigenetic information of patients, which limits the in-depth understanding of disease mechanisms and the formulation of effective treatment strategies. Traditional decision support systems usually lack comprehensive consideration of multi-source data and complex clinical situations, resulting in clinical decisions that are not comprehensive and precise enough. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose an intelligent clinical test result interpretation system.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An intelligent clinical test result interpretation system includes a data acquisition module, a preliminary analysis module, a deep pattern recognition module, a disease prediction module, a personalized treatment module, a treatment effect monitoring module, an epigenetics analysis module, a knowledge integration and decision support module;

[0007] The data acquisition module is based on clinical test results and patient medical records, and uses data mining technology to collect and integrate data, generating a comprehensive data set;

[0008] The preliminary analysis module is based on the comprehensive data set, and uses statistical analysis and time series analysis methods to perform data trend analysis, generating a preliminary analysis report;

[0009] The deep pattern recognition module is based on the preliminary analysis report, and uses deep learning algorithms to perform data pattern recognition, generating a deep pattern recognition result;

[0010] The disease prediction module is based on the deep pattern recognition result, and uses gradient boosting machines to predict the disease development trend, generating a disease prediction report;

[0011] The personalized treatment module is based on the disease prediction report and the patient's clinical data, and uses personalized algorithms to design treatment plans, generating personalized treatment plans;

[0012] The treatment effect monitoring module is based on the personalized treatment plan, and evaluates the treatment effect by tracking the patient's treatment response and using real-time monitoring and data analysis methods, generating a treatment effect evaluation report;

[0013] The epigenetics analysis module is based on the treatment effect evaluation report and the patient's epigenetics data, and uses bioinformatics methods for in-depth analysis, generating an epigenetics analysis report;

[0014] The knowledge integration and decision support module is based on the epigenetics analysis report, the comprehensive data set, the preliminary analysis report, the deep pattern recognition result, the personalized treatment plan, and the treatment effect evaluation report, and uses knowledge management and decision support technologies to provide suggestions for clinical decision-making, generating a decision support report.

[0015] As a further aspect of the present invention, the comprehensive data set includes clinical test parameters, patient medical history records, and real-time physiological monitoring data. The deep mode recognition results include potential health risk patterns, key biomarkers, and predictive health indicators. The disease prediction report includes future health risk assessment, disease development prediction path, and preventive intervention suggestions. The personalized treatment plan includes customized drug selection, treatment plan adjustment suggestions, and patient treatment monitoring plan. The treatment effect evaluation report includes treatment response metrics, adverse reaction monitoring, and treatment adjustment suggestions. The epigenetics analysis report includes DNA methylation patterns, genetic risk assessment, and associated biomarker identification. The decision support report includes treatment strategy optimization suggestions, patient management strategies, and implications for future research directions.

[0016] As a further aspect of the present invention, the data acquisition module includes a data interface sub-module, a medical record analysis sub-module, a real-time data acquisition sub-module, a data cleaning sub-module, and a data standardization sub-module;

[0017] Based on clinical test results and patient medical records, the data interface sub-module uses data integration technology to integrate data and generate an original data set;

[0018] Based on the original data set, the medical record analysis sub-module uses text analysis technology to extract information and generate a patient health profile;

[0019] Based on the patient health profile, the real-time data acquisition sub-module uses physiological signal processing technology to perform real-time monitoring and generate real-time monitoring data;

[0020] Based on the real-time monitoring data, the data cleaning sub-module uses data purification technology to optimize data and generate purified data;

[0021] Based on the purified data, the data standardization sub-module uses data formatting technology to generate a comprehensive data set;

[0022] The data integration technology includes heterogeneous data source adaptation, ETL processes, and data warehouse construction. The text analysis technology specifically includes semantic analysis, keyword mining, and context recognition. The physiological signal processing technology includes signal denoising, waveform analysis, and real-time data stream encoding. The data purification technology specifically includes outlier removal, data normalization, and duplicate data elimination. The data formatting technology specifically includes data mapping, data alignment, and encoding conversion.

[0023] As a further aspect of the present invention, the preliminary analysis module includes a statistical analysis sub-module, a time series analysis sub-module, a trend recognition sub-module, an anomaly detection sub-module, and a correlation analysis sub-module;

[0024] The statistical analysis sub-module performs data feature analysis based on the comprehensive data set using statistical modeling techniques to generate statistical analysis results;

[0025] The time series analysis sub-module performs trend analysis based on the statistical analysis results using time series prediction techniques to generate a time series analysis report;

[0026] The trend recognition sub-module performs trend recognition based on the time series analysis report using data trend mining techniques to generate trend recognition results;

[0027] The anomaly detection sub-module performs anomaly analysis based on the trend recognition results using anomaly point recognition techniques to generate an anomaly detection report;

[0028] The correlation analysis sub-module generates a preliminary analysis report based on the anomaly detection report using association rule analysis techniques;

[0029] The statistical modeling techniques include multivariate analysis, regression analysis, and hypothesis testing. The time series prediction techniques include autoregressive moving average model, seasonal decomposition, and volatility modeling. The data trend mining techniques include linear trend analysis and non-linear trend detection. The anomaly point recognition techniques specifically include clustering-based anomaly detection, density-based outlier identification, and time series anomaly point detection. The association rule analysis techniques include support and confidence calculation, frequent item set mining, and rule generation.

[0030] As a further solution of the present invention, the deep mode recognition module includes a feature extraction sub-module, a mode classification sub-module, a deep learning training sub-module, a model optimization sub-module, and a result verification sub-module;

[0031] The feature extraction sub-module extracts key features based on the preliminary analysis report using feature selection and dimensionality reduction techniques to generate a feature set;

[0032] The mode classification sub-module classifies data patterns based on the feature set using supervised learning classification algorithms to generate mode classification results;

[0033] The deep learning training sub-module trains data patterns based on the mode classification results using deep neural networks to generate a training model;

[0034] The model optimization sub-module optimizes the model performance parameters based on the training model using hyperparameter tuning and cross-validation to generate an optimized model;

[0035] The result verification sub-module verifies the model effect based on the optimized model using performance metric evaluation to perform result verification and generate deep mode recognition results;

[0036] The feature selection and dimensionality reduction techniques include principal component analysis, linear discriminant analysis, and autoencoders. The supervised learning classification algorithms are specifically logistic regression, random forest, and gradient boosting decision tree. The deep neural networks are specifically convolutional neural network and recurrent neural network. The hyperparameter tuning and cross-validation include grid search, Bayesian optimization, and K-fold cross-validation. The performance metric evaluation includes accuracy, recall, and F1-score evaluation.

[0037] As a further aspect of the present invention, the disease prediction module includes a disease trend analysis sub-module, a risk assessment sub-module, a prediction model training sub-module, a prediction result verification sub-module, and an early warning sub-module;

[0038] The disease trend analysis sub-module performs trend analysis based on the deep pattern recognition results using statistical trend analysis and generates a trend analysis report;

[0039] The risk assessment sub-module evaluates the disease risk based on the trend analysis report using a risk scoring model and generates a risk assessment report;

[0040] The prediction model training sub-module trains a disease prediction model based on the risk assessment report using gradient boosting machines and generates a trained prediction model;

[0041] The prediction result verification sub-module verifies the prediction accuracy based on the trained prediction model using model evaluation techniques and generates a prediction result verification report;

[0042] The early warning sub-module provides early warning of the disease, conducts risk notification, and generates a disease prediction report based on the prediction result verification report using a real-time monitoring and alert system;

[0043] The statistical trend analysis includes moving average and exponential smoothing methods. The risk scoring model includes Cox proportional hazards model and risk stratification method. The gradient boosting machines are specifically XGBoost, LightGBM, and CatBoost. The model evaluation techniques include confusion matrix and ROC-AUC curve analysis. The real-time monitoring and alert system includes threshold-triggered alerts and real-time data monitoring.

[0044] As a further aspect of the present invention, the personalized treatment module includes a treatment plan design sub-module, a patient data analysis sub-module, a treatment plan optimization sub-module, a treatment effect prediction sub-module, and a patient feedback analysis sub-module;

[0045] The treatment plan design sub-module conducts a preliminary design of a personalized treatment plan based on the disease prediction report and the patient's clinical data using a multi-factor decision tree algorithm, and conducts a preliminary evaluation of the plan to generate a draft of the personalized treatment plan;

[0046] The patient data analysis sub-module, based on the draft personalized treatment plan, uses cluster analysis methods to conduct data segmentation and feature analysis of patients, and makes fine-tuning of the plan to generate a patient data analysis report;

[0047] The treatment plan optimization sub-module, based on the patient data analysis report, uses genetic algorithms to deeply optimize the treatment plan and evaluate the optimization effect, generating an optimized personalized treatment plan;

[0048] The treatment effect prediction sub-module, based on the optimized personalized treatment plan, uses a Bayesian network prediction model to conduct probability prediction of treatment effects and analyze the prediction results, generating a treatment effect prediction report;

[0049] The patient feedback analysis sub-module, based on the treatment effect prediction report, uses sentiment analysis technology to conduct sentiment analysis of patient feedback and comprehensively analyze the feedback content, generating a comprehensive patient feedback analysis report;

[0050] The multi-factor decision tree algorithm includes decision tree construction based on disease severity classification, calculation of patient individual difference factor weights, and analysis of historical treatment response patterns. The cluster analysis methods include K-means clustering and hierarchical cluster analysis. The genetic algorithm specifically includes gene coding optimization based on patients' historical treatment responses, fitness function design, crossover, and mutation operations. The Bayesian network prediction model includes construction of conditional probability tables, network parameter learning based on historical data, and probability inference of treatment plan effects. The sentiment analysis technology is specifically the sentiment recognition algorithm in natural language processing.

[0051] As a further solution of the present invention, the treatment effect monitoring module includes a real-time monitoring sub-module, an effect evaluation sub-module, a data update sub-module, a feedback integration sub-module, and an adjustment suggestion sub-module;

[0052] The real-time monitoring sub-module, based on the optimized personalized treatment plan, uses biosignal monitoring technology to conduct real-time monitoring of the patient's treatment process and real-time analysis of data, generating real-time health monitoring data;

[0053] The effect evaluation sub-module, based on the real-time health monitoring data, uses statistical analysis methods to conduct quantitative evaluation of treatment effects and comprehensive analysis of effects, generating a comprehensive treatment effect evaluation report;

[0054] The data update sub-module, based on the comprehensive treatment effect evaluation report, uses database update technology to update and integrate the patient's treatment data and conduct data consistency checks, generating updated patient data;

[0055] The feedback integration sub-module conducts in-depth integration analysis of treatment feedback and evaluates the integration results by using data fusion technology based on the updated patient data and the comprehensive analysis report of patient feedback, and generates a treatment feedback integration analysis report;

[0056] The adjustment suggestion sub-module formulates adjustment suggestions for the treatment plan by using a decision support system based on the treatment feedback integration analysis report, analyzes the effectiveness of the suggestions, and generates a treatment adjustment suggestion report;

[0057] The biological signal monitoring technology includes real-time acquisition of multi-parameter physiological signals, signal quality assessment, and health status analysis based on bioelectrical signals. The statistical analysis methods are specifically time series analysis, survival analysis, and construction of a multi-variable regression model based on treatment effects. The database update technology includes data synchronization mechanisms in cloud databases, dynamic data update algorithms, and data consistency and integrity checks. The data fusion technology is specifically multi-source data integration, comprehensive analysis based on historical and current data, and dimension reduction and fusion strategies of data dimensions. The decision support system includes a rule engine in an expert system, pattern recognition based on artificial intelligence, and simulation of treatment plan adjustment.

[0058] As a further solution of the present invention, the epigenetics analysis module includes a DNA methylation analysis sub-module, a histone modification analysis sub-module, a biomarker identification sub-module, a data fusion sub-module, and a genetic risk assessment sub-module;

[0059] The DNA methylation analysis sub-module analyzes DNA methylation patterns by using a Bayesian network based on patient epigenetics data and generates DNA methylation analysis results;

[0060] The histone modification analysis sub-module analyzes histone modification patterns by using mass spectrometry analysis and cross-immunoprecipitation technology based on the DNA methylation analysis results and generates histone modification analysis results;

[0061] The biomarker identification sub-module identifies target biomarkers by using gene sequence alignment and expression profile analysis based on the histone modification analysis results and generates biomarker identification results;

[0062] The data fusion sub-module integrates multi-source biological data by using multi-dimensional data integration and statistical analysis technology based on the biomarker identification results and generates data fusion results;

[0063] The genetic risk assessment sub-module conducts risk assessment by using population genetics analysis and phenotype association studies based on the data fusion results and the treatment effect assessment report and generates a genetic risk assessment report;

[0064] The Bayesian network includes structure learning, parameter learning, and inference algorithms. The mass spectrometry and cross - immunoprecipitation technology includes peptide separation, mass detection, and protein - interaction network analysis. The gene sequence alignment and expression profile analysis includes sequence homology analysis, differential expression gene screening, and functional annotation. The multi - dimensional data integration and statistical analysis technology includes heterogeneous data mapping, correlation analysis, and pattern recognition. The population genetics analysis and phenotype - association study includes genetic variant screening, phenotype data association, and risk prediction model establishment.

[0065] As a further aspect of the present invention, the knowledge integration and decision - support module includes a knowledge management sub - module, a data visualization sub - module, a decision - recommendation generation sub - module, a medical knowledge - base update sub - module, and a clinical application interface sub - module;

[0066] The knowledge management sub - module, based on the genetic risk assessment report, applies knowledge - graph construction and semantic analysis technologies to perform data integration and analysis, and generates knowledge management results;

[0067] The data visualization sub - module, based on the knowledge management results, uses interactive visualization and data exploration technologies to display data and analysis results, and generates a data visualization report;

[0068] The decision - recommendation generation sub - module, based on the data visualization report and the comprehensive data set, adopts expert systems and machine - learning algorithms to propose treatment recommendations and generate a decision - making plan;

[0069] The medical knowledge - base update sub - module, based on the decision - making plan and the deep pattern - recognition results, uses automated literature mining and data - mining technologies to update the medical knowledge - base and complete the update of the medical knowledge - base;

[0070] The clinical application interface sub - module, based on the medical knowledge - base update results and the personalized treatment plan, develops API and data standardization technologies, and generates a clinical application interface report;

[0071] The knowledge - graph construction and semantic analysis technologies include entity recognition, relationship extraction, and knowledge reasoning. The interactive visualization and data exploration technologies include chart generation, user - interface design, and data interaction. The expert systems and machine - learning algorithms include rule - based reasoning, pattern recognition, and prediction modeling. The automated literature mining and data - mining technologies include literature screening, key - information extraction, and knowledge update. The API and data standardization technologies include interface design, data - format unification, and system integration.

[0072] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0073] In the present invention, statistical analysis and time series analysis are utilized to conduct a detailed analysis of data trends, improving the accuracy and efficiency of preliminary diagnosis. The application of deep learning algorithms in pattern recognition enables more precise identification of complex disease patterns, especially excelling in early lesion detection. The use of gradient boosting machines in disease prediction provides a powerful tool for predicting the development trend of diseases. The formulation of personalized treatment plans fully considers the specific conditions of patients, making the treatment more precise and effective. The application of real-time monitoring and data analysis methods provides instant feedback to doctors in the evaluation of treatment effects, making treatment adjustments more flexible and timely. The incorporation of epigenetics analysis offers a new perspective for understanding the molecular mechanisms of diseases and treatment responses. The application of the knowledge integration and decision support module provides comprehensive decision support to doctors, improving the quality and efficiency of clinical decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is the system flowchart of the present invention;

[0075] Figure 2 is the schematic diagram of the system framework of the present invention;

[0076] Figure 3 is the flowchart of the data acquisition module of the present invention;

[0077] Figure 4 is the flowchart of the preliminary analysis module of the present invention;

[0078] Figure 5 is the flowchart of the deep pattern recognition module of the present invention;

[0079] Figure 6 is the flowchart of the disease prediction module of the present invention;

[0080] Figure 7 is the flowchart of the personalized treatment module of the present invention;

[0081] Figure 8 is the flowchart of the treatment effect monitoring module of the present invention;

[0082] Figure 9 is the flowchart of the epigenetics analysis module of the present invention;

[0083] Figure 10 is the flowchart of the knowledge integration and decision support module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0084] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0085] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0086] Embodiment 1

[0087] Please refer to Figures 1 to 2 , an intelligent clinical test result interpretation system includes a data collection module, a preliminary analysis module, a deep pattern recognition module, a disease prediction module, a personalized treatment module, a treatment effect monitoring module, an epigenetics analysis module, a knowledge integration and decision support module;

[0088] The data collection module, based on clinical test results and patient medical records, uses data mining techniques to collect and integrate data and generate a comprehensive data set;

[0089] The preliminary analysis module, based on the comprehensive data set, uses statistical analysis and time series analysis methods to perform data trend analysis and generate a preliminary analysis report;

[0090] The deep pattern recognition module, based on the preliminary analysis report, uses deep learning algorithms to perform data pattern recognition and generate a deep pattern recognition result;

[0091] The disease prediction module, based on the deep pattern recognition result, uses gradient boosting machines to predict the disease development trend and generate a disease prediction report;

[0092] The personalized treatment module, based on the disease prediction report and the patient's clinical data, uses personalized algorithms to design treatment plans and generate personalized treatment plans;

[0093] The treatment effect monitoring module, based on the personalized treatment plan, evaluates the treatment effect by tracking the patient's treatment response and using real-time monitoring and data analysis methods, and generates a treatment effect evaluation report;

[0094] The epigenetics analysis module, based on the treatment effect evaluation report and the patient's epigenetics data, uses bioinformatics methods for in-depth analysis and generates an epigenetics analysis report;

[0095] The Knowledge Integration and Decision Support Module provides suggestions for clinical decision-making and generates decision support reports by using knowledge management and decision support technologies based on epigenetics analysis reports, comprehensive datasets, preliminary analysis reports, in-depth pattern recognition results, personalized treatment plans, and treatment effect evaluation reports.

[0096] The comprehensive dataset includes clinical test parameters, patient medical history records, and real-time physiological monitoring data. The in-depth pattern recognition results include potential health risk patterns, key biomarkers, and predictive health indicators. The disease prediction report includes future health risk assessment, disease development prediction path, and preventive intervention suggestions. The personalized treatment plan includes customized drug selection, treatment plan adjustment suggestions, and patient treatment monitoring plan. The treatment effect evaluation report includes treatment response metrics, adverse reaction monitoring, and treatment adjustment suggestions. The epigenetics analysis report includes DNA methylation patterns, genetic risk assessment, and identification of associated biomarkers. The decision support report includes suggestions for optimizing treatment strategies, patient management strategies, and implications for future research directions.

[0097] Through comprehensive data integration and in-depth analysis, the system helps improve the accuracy and timeliness of clinical decision-making. Doctors can detect potential health problems earlier, formulate more effective treatment plans, and increase the early diagnosis rate and treatment success rate of diseases. The personalized treatment module formulates customized treatment plans for each patient, taking into account their unique clinical data and disease prediction situation. This not only reduces unnecessary treatments and drug use but also improves the pertinence and effectiveness of treatment, alleviating patient discomfort and adverse reactions. The treatment effect monitoring module tracks the treatment response of patients in real time, discovers problems in a timely manner, and provides treatment adjustment suggestions. This helps doctors better manage the treatment process of patients and ensure the best medical care. The epigenetics analysis module provides doctors with more information about the genetic characteristics of patients, helping them better understand the biological characteristics and health risks of patients. This is crucial for formulating personalized treatment plans and predicting the future health status of patients. The integrated knowledge and decision support module provides a powerful auxiliary tool, offering doctors more information and suggestions to support clinical decision-making. This helps improve the quality of healthcare, reduce medical errors, and increase patient satisfaction.

[0098] Please refer to Figure 3 , the data acquisition module includes a data interface sub-module, a medical record analysis sub-module, a real-time data acquisition sub-module, a data cleaning sub-module, and a data standardization sub-module;

[0099] The data interface sub-module integrates data based on clinical test results and patient medical records using data integration technology to generate an original dataset;

[0100] Based on the original dataset, the medical record analysis sub-module uses text analysis technology to extract information and generate patient health records;

[0101] Based on the patient health records, the real-time data collection sub-module uses physiological signal processing technology for real-time monitoring and generates real-time monitoring data;

[0102] Based on the real-time monitoring data, the data cleaning sub-module uses data purification technology to optimize the data and generate purified data;

[0103] Based on the purified data, the data standardization sub-module uses data formatting technology to generate a comprehensive dataset;

[0104] Data integration technology includes heterogeneous data source adaptation, ETL processes, and data warehouse construction. Text analysis technology specifically includes semantic analysis, keyword mining, and context recognition. Physiological signal processing technology includes signal denoising, waveform analysis, and real-time data stream encoding. Data purification technology specifically includes outlier removal, data normalization, and duplicate data elimination. Data formatting technology specifically includes data mapping, data alignment, and encoding conversion.

[0105] Based on clinical test results and patient medical records, the data interface sub-module uses data integration technology to integrate data and generate an original dataset. This process involves heterogeneous data source adaptation, ETL (Extract, Transform, Load) processes, and data warehouse construction. The data interface sub-module ensures that the system can obtain data from different sources and integrate it into an actionable original dataset.

[0106] Based on the original dataset, the medical record analysis sub-module applies text analysis technology to extract information and generate the patient's health records. Text analysis technology includes semantic analysis, keyword mining, and context recognition. This helps the system understand the key information in the medical records and provides important data for subsequent analysis and prediction.

[0107] Based on the patient's health records, the real-time data collection sub-module uses physiological signal processing technology for real-time monitoring and generates real-time monitoring data. Physiological signal processing technology includes signal denoising, waveform analysis, and real-time data stream encoding. This enables the system to track the patient's physiological condition and capture real-time data about their health.

[0108] Based on the real-time monitoring data, the data cleaning sub-module uses data purification technology to optimize the data and generate purified data. Data purification technology includes outlier removal, data normalization, and duplicate data elimination. This helps ensure the quality and accuracy of the data, reducing potential noise and misleading information.

[0109] The data normalization sub-module generates a comprehensive data set based on the purified data using data formatting techniques. The data formatting techniques include data mapping, data alignment, and encoding conversion. This ensures that the system can integrate the data into a consistent format for subsequent analysis and applications.

[0110] Please refer to Figure 4 , the preliminary analysis module includes a statistical analysis sub-module, a time series analysis sub-module, a trend recognition sub-module, an anomaly detection sub-module, and a correlation analysis sub-module;

[0111] The statistical analysis sub-module performs data feature analysis based on the comprehensive data set using statistical modeling techniques and generates statistical analysis results;

[0112] The time series analysis sub-module performs trend analysis based on the statistical analysis results using time series prediction techniques and generates a time series analysis report;

[0113] The trend recognition sub-module performs trend recognition based on the time series analysis report using data trend mining techniques and generates trend recognition results;

[0114] The anomaly detection sub-module performs anomaly analysis based on the trend recognition results using anomaly point recognition techniques and generates an anomaly detection report;

[0115] The correlation analysis sub-module generates a preliminary analysis report based on the anomaly detection report using association rule analysis techniques;

[0116] Statistical modeling techniques include multivariate analysis, regression analysis, and hypothesis testing. Time series prediction techniques include autoregressive integrated moving average models, seasonal decomposition, and volatility modeling. Data trend mining techniques include linear trend analysis and non-linear trend detection. Anomaly point recognition techniques specifically include clustering-based anomaly detection, density-based outlier identification, and time series anomaly point detection. Association rule analysis techniques include support and confidence calculation, frequent item set mining, and rule generation.

[0117] The statistical analysis sub-module performs data feature analysis based on the comprehensive data set using statistical modeling techniques and generates statistical analysis results. This includes methods such as multivariate analysis, regression analysis, and hypothesis testing, which are used to identify key features and trends in the data.

[0118] The time series analysis sub-module performs trend analysis based on the statistical analysis results using time series prediction techniques and generates a time series analysis report. Time series prediction techniques include autoregressive integrated moving average models, seasonal decomposition, and volatility modeling, which are used to identify the time trends in the data.

[0119] The trend recognition sub-module conducts trend recognition and generates trend recognition results based on the time series analysis report, using data trend mining techniques. This involves linear trend analysis and non-linear trend detection to capture trend changes in the data.

[0120] The anomaly detection sub-module conducts anomaly analysis and generates an anomaly detection report based on the trend recognition results, using anomaly point recognition techniques. Anomaly point recognition techniques include clustering-based anomaly detection, density-based outlier identification, and time series anomaly point detection to identify potential abnormal data.

[0121] The correlation analysis sub-module generates a preliminary analysis report based on the anomaly detection report, using association rule analysis techniques. Association rule analysis techniques include support and confidence calculation, frequent item set mining, and rule generation to discover correlations in the data.

[0122] Please refer to Figure 5 , the deep mode recognition module includes a feature extraction sub-module, a mode classification sub-module, a deep learning training sub-module, a model optimization sub-module, and a result verification sub-module;

[0123] The feature extraction sub-module extracts key features and generates a feature set based on the preliminary analysis report, using feature selection and dimensionality reduction techniques.

[0124] The mode classification sub-module classifies data patterns and generates mode classification results based on the feature set, using supervised learning classification algorithms.

[0125] The deep learning training sub-module trains data patterns and generates a training model based on the mode classification results, using a deep neural network.

[0126] The model optimization sub-module optimizes the model performance parameters and generates an optimized model based on the training model, using hyperparameter tuning and cross-validation.

[0127] The result verification sub-module verifies the model effect, conducts result verification, and generates deep mode recognition results based on the optimized model, using performance metric evaluation.

[0128] Feature selection and dimensionality reduction techniques include principal component analysis, linear discriminant analysis, and autoencoders. The supervised learning classification algorithms are specifically logistic regression, random forest, and gradient boosting decision tree. The deep neural network is specifically a convolutional neural network and a recurrent neural network. Hyperparameter tuning and cross-validation include grid search, Bayesian optimization, and K-fold cross-validation. Performance metric evaluation includes accuracy, recall, and F1-score evaluation.

[0129] Based on the preliminary analysis report, the feature extraction sub-module uses feature selection and dimensionality reduction techniques to extract key features and generate a feature set. Feature extraction techniques include principal component analysis, linear discriminant analysis, and autoencoders, which are used to capture the most representative information in the data.

[0130] Based on the feature set, the pattern classification sub-module uses supervised learning classification algorithms to classify data patterns and generate pattern classification results. Supervised learning classification algorithms include logistic regression, random forest, and gradient boosting decision tree, which are used to divide the data into different categories.

[0131] Based on the pattern classification results, the deep learning training sub-module uses deep neural networks to perform model training and generate a trained model. Deep neural networks can be convolutional neural networks, recurrent neural networks, etc., which are used to learn complex data patterns.

[0132] Based on the trained model, the model optimization sub-module uses hyperparameter tuning and cross-validation to optimize the model performance and generate an optimized model. Hyperparameter tuning and cross-validation techniques help improve the generalization ability of the model.

[0133] Based on the optimized model, the result verification sub-module uses performance metric evaluation to verify the model effectiveness and generate deep pattern recognition results. Performance metric evaluation includes accuracy, recall, and F1-score evaluation, which are used to measure the performance of the model.

[0134] Please refer to Figure 6 , the disease prediction module includes a disease trend analysis sub-module, a risk assessment sub-module, a prediction model training sub-module, a prediction result verification sub-module, and an early warning sub-module;

[0135] Based on the deep pattern recognition results, the disease trend analysis sub-module uses statistical trend analysis to perform trend analysis and generate a trend analysis report;

[0136] Based on the trend analysis report, the risk assessment sub-module uses a risk scoring model to evaluate disease risk and generate a risk assessment report;

[0137] Based on the risk assessment report, the prediction model training sub-module uses gradient boosting machines to train a disease prediction model and generate a trained prediction model;

[0138] Based on the trained prediction model, the prediction result verification sub-module uses model evaluation techniques to verify the prediction accuracy and generate a prediction result verification report;

[0139] Based on the prediction result verification report, the early warning sub-module uses a real-time monitoring and alert system to provide early disease warnings, conduct risk notifications, and generate a disease prediction report;

[0140] Statistical trend analysis includes moving average and exponential smoothing methods. The risk scoring model includes the Cox proportional hazards model and risk stratification methods. Gradient boosting machines specifically refer to XGBoost, LightGBM, and CatBoost. Model evaluation techniques include confusion matrices and ROC-AUC curve analysis. The real-time monitoring and alert system includes threshold-triggered alerts and real-time data monitoring.

[0141] Disease Trend Analysis Sub-module

[0142] Technology: Statistical Trend Analysis

[0143] Methods: Moving average and exponential smoothing.

[0144] Operation Steps:

[0145] Data Collection: Collect disease-related data.

[0146] Data Preprocessing: Clean and format the data.

[0147] Trend Analysis: Apply moving average and exponential smoothing methods to analyze trends.

[0148] Code Example (Python):

[0149] import pandas as pd

[0150] # Load data

[0151] data = pd.read_csv('disease_data.csv') # Replace with the actual data file

[0152] time_series = data['disease_count'] # Replace with the disease occurrence count column

[0153] # Moving average

[0154] rolling_mean = time_series.rolling(window = 4).mean() # The window size can be adjusted according to the data

[0155] # Exponential smoothing

[0156] exp_smooth = time_series.ewm(span = 4).mean() # The span can be adjusted according to the data Risk Assessment Sub-module

[0157] Technology: Risk Scoring Model

[0158] Methods: Cox proportional hazards model and risk stratification.

[0159] Operation steps:

[0160] Trend analysis report parsing: Parse the trend analysis report.

[0161] Apply the Cox proportional hazards model: Build and train the model.

[0162] Code example:

[0163] from lifelines import CoxPHFitter

[0164] # Assume data contains the required features and target variable

[0165] cox_model = CoxPHFitter()

[0166] cox_model.fit(data, duration_col='duration', event_col='event')

[0167] risk_scores = cox_model.predict_partial_hazard(data)

[0168] Prediction model training sub-module

[0169] Technology: Gradient Boosting Machine

[0170] Methods: XGBoost, LightGBM, CatBoost.

[0171] Operation steps:

[0172] Risk assessment report parsing: Parse the risk assessment report.

[0173] Model training: Train the model using the gradient boosting method.

[0174] Code example (taking XGBoost as an example):

[0175] import xgboost as xgb

[0176] # Assume data contains the required features and target variable

[0177] dtrain = xgb.DMatrix(data.drop('target', axis = 1), label = data['target']) # Parameter settings

[0178] params = {'max_depth': 3, 'eta': 0.1, 'objective': 'binary:logistic'} num_round = 10 # Number of training rounds

[0179] # Train the model

[0180] bst = xgb.train(params, dtrain, num_round)

[0181] Prediction result verification sub-module

[0182] Technology: Model evaluation

[0183] Method: Confusion matrix, ROC-AUC curve analysis.

[0184] Operation steps:

[0185] Use the test dataset: Evaluate the model.

[0186] Calculate evaluation metrics: Calculate the confusion matrix and ROC-AUC.

[0187] Code example:

[0188] from sklearn.metrics import confusion_matrix, roc_auc_score

[0189] # Assume y_true is the true label and y_pred is the model's predicted label

[0190] y_true = [1, 0, 1, 1, 0]

[0191] y_pred = bst.predict(dtest)

[0192] # Confusion matrix

[0193] conf_matrix = confusion_matrix(y_true, y_pred)

[0194] # ROC-AUC score

[0195] roc_auc = roc_auc_score(y_true, y_pred)

[0196] Early warning sub-module

[0197] Technology: Real-time monitoring and alarm system

[0198] Method: Threshold-triggered alarm and real-time data monitoring.

[0199] Operation steps:

[0200] Set the threshold: Set the alarm threshold according to the output of the prediction model.

[0201] Real-time monitoring: Monitor real-time data and trigger an alarm.

[0202] Code example:

[0203] # Threshold setting

[0204] threshold = 0.5 # Adjust according to the actual situation

[0205] # Real-time monitoring and alarm

[0206] real_time_data =... # Real-time data collection

[0207] prediction = bst.predict(real_time_data)

[0208] if prediction > threshold:

[0209] trigger_alert('High disease risk detected!')

[0210] Please refer to Figure 7 , the personalized treatment module includes a treatment plan design sub-module, a patient data analysis sub-module, a treatment plan optimization sub-module, a treatment effect prediction sub-module, and a patient feedback analysis sub-module;

[0211] The treatment plan design sub-module is based on the disease prediction report and the patient's clinical data, uses the multi-factor decision tree algorithm to conduct a preliminary design of the personalized treatment plan, and conducts a preliminary evaluation of the plan to generate a draft of the personalized treatment plan;

[0212] The patient data analysis sub-module is based on the draft of the personalized treatment plan, uses the clustering analysis method to conduct data segmentation and feature analysis of the patient, and conducts a fine-tuning of the plan to generate a patient data analysis report;

[0213] The treatment plan optimization sub-module is based on the patient data analysis report, uses the genetic algorithm to conduct in-depth optimization of the treatment plan, and conducts an evaluation of the optimization effect to generate an optimized personalized treatment plan;

[0214] The treatment effect prediction sub-module is based on the optimized personalized treatment plan, uses the Bayesian network prediction model to conduct a probability prediction of the treatment effect, and conducts an analysis of the prediction results to generate a treatment effect prediction report;

[0215] Based on the treatment effect prediction report, the patient feedback analysis sub-module uses sentiment analysis technology to conduct sentiment analysis of patient feedback, comprehensively analyze the feedback content, and generate a comprehensive patient feedback analysis report;

[0216] The multi-factor decision tree algorithm includes decision tree construction based on disease severity classification, calculation of patient individual difference factor weights, and analysis of historical treatment response patterns. The clustering analysis method specifically includes K-means clustering and hierarchical clustering analysis. The genetic algorithm is specifically gene coding optimization based on patients' historical treatment responses, fitness function design, crossover and mutation operations. The Bayesian network prediction model includes construction of conditional probability tables, network parameter learning based on historical data, and probability inference of treatment plan effects. The sentiment analysis technology is specifically the sentiment recognition algorithm in natural language processing.

[0217] Based on the disease prediction report and the patient's clinical data, the treatment plan design sub-module uses the multi-factor decision tree algorithm to preliminarily design a personalized treatment plan. Considering factors such as disease severity and patient individual differences, a draft personalized treatment plan is generated.

[0218] Based on the draft personalized treatment plan, the patient data analysis sub-module uses the clustering analysis method to subdivide and analyze the patient's data, further fine-tune the treatment plan, and generate a patient data analysis report.

[0219] Based on the patient data analysis report, the treatment plan optimization sub-module uses the genetic algorithm to deeply optimize the treatment plan. The genetic algorithm considers factors such as the patient's historical treatment response pattern and generates an optimized personalized treatment plan.

[0220] Based on the optimized personalized treatment plan, the treatment effect prediction sub-module uses the Bayesian network prediction model to conduct probability prediction of treatment effects. This helps doctors understand the treatment results and analyze the prediction results.

[0221] Based on the treatment effect prediction report, the patient feedback analysis sub-module uses sentiment analysis technology to conduct sentiment analysis of patient feedback. By analyzing the patient's emotional feedback, the patient's needs and experiences can be better understood.

[0222] Please refer to Figure 8 , the treatment effect monitoring module includes a real-time monitoring sub-module, an effect evaluation sub-module, a data update sub-module, a feedback integration sub-module, and an adjustment suggestion sub-module;

[0223] Based on the optimized personalized treatment plan, the real-time monitoring sub-module uses biometric signal monitoring technology to conduct real-time monitoring of the patient's treatment process and real-time analysis of the data, generating real-time health monitoring data;

[0224] The effect evaluation sub-module conducts a quantitative evaluation of the treatment effect based on real-time health monitoring data using statistical analysis methods, and conducts a comprehensive analysis of the effect to generate a comprehensive treatment effect evaluation report;

[0225] The data update sub-module updates and integrates the patient's treatment data using database update technology based on the comprehensive treatment effect evaluation report, and conducts data consistency checks to generate updated patient data;

[0226] The feedback integration sub-module conducts a deep integration analysis of treatment feedback using data fusion technology based on the updated patient data and the comprehensive patient feedback analysis report, and evaluates the integration results to generate a treatment feedback integration analysis report;

[0227] The adjustment suggestion sub-module formulates adjustment suggestions for the treatment plan using a decision support system based on the treatment feedback integration analysis report, and conducts an effectiveness analysis of the suggestions to generate a treatment adjustment suggestion report;

[0228] The biosignal monitoring technology includes real-time acquisition of multi-parameter physiological signals, signal quality assessment, and health status analysis based on bioelectrical signals. The statistical analysis methods are specifically time series analysis, survival analysis, and construction of a multivariate regression model based on treatment effects. The database update technology includes data synchronization mechanisms in cloud databases, dynamic data update algorithms, and data consistency and integrity checks. The data fusion technology is specifically multi-source data integration, comprehensive analysis based on historical and current data, and dimensionality reduction and fusion strategies for data dimensions. The decision support system includes a rule engine in an expert system, pattern recognition based on artificial intelligence, and simulation of treatment plan adjustment.

[0229] The real-time monitoring sub-module monitors the patient's treatment process in real time based on the patient's personalized treatment plan using biosignal monitoring technology. By collecting and analyzing physiological signal data in real time, real-time health monitoring data is generated to promptly understand the patient's treatment situation.

[0230] The effect evaluation sub-module is based on real-time health monitoring data. This sub-module uses statistical analysis methods to quantitatively evaluate the treatment effect. This helps doctors and the system assess the progress of the treatment and generate a comprehensive treatment effect evaluation report, providing valuable feedback information.

[0231] The data update sub-module updates and integrates the patient's treatment data using database update technology according to the comprehensive treatment effect evaluation report. This ensures that the patient's records are always up-to-date and conducts data consistency checks to ensure the accuracy and integrity of the data.

[0232] The feedback integration sub-module conducts in-depth integration analysis using data fusion technology based on the updated patient data and the comprehensive analysis report of patient feedback. By integrating data from different sources, a more comprehensive understanding of the patient's treatment situation can be achieved, and a treatment feedback integration analysis report can be generated.

[0233] The adjustment suggestion sub-module formulates adjustment suggestions for the treatment plan using a decision support system based on the treatment feedback integration analysis report. These suggestions are formulated according to the data and feedback information analyzed by the system, aiming to optimize the treatment plan. The effectiveness of the suggestions will also be evaluated.

[0234] Please refer to Figure 9 , the epigenetics analysis module includes a DNA methylation analysis sub-module, a histone modification analysis sub-module, a biomarker identification sub-module, a data fusion sub-module, and a genetic risk assessment sub-module;

[0235] The DNA methylation analysis sub-module analyzes DNA methylation patterns using a Bayesian network based on the patient's epigenetics data and generates DNA methylation analysis results;

[0236] The histone modification analysis sub-module analyzes histone modification patterns using mass spectrometry analysis and cross-linked immunoprecipitation technology based on the DNA methylation analysis results and generates histone modification analysis results;

[0237] The biomarker identification sub-module identifies target biomarkers using gene sequence alignment and expression profile analysis based on the histone modification analysis results and generates biomarker identification results;

[0238] The data fusion sub-module integrates multi-source biological data using multi-dimensional data integration and statistical analysis technology based on the biomarker identification results and generates data fusion results;

[0239] The genetic risk assessment sub-module conducts risk assessment using population genetics analysis and phenotype association studies based on the data fusion results and the treatment effect evaluation report and generates a genetic risk assessment report;

[0240] The Bayesian network includes structure learning, parameter learning, and inference algorithms. Mass spectrometry analysis and cross-linked immunoprecipitation technology include peptide separation, mass detection, and protein interaction network analysis. Gene sequence alignment and expression profile analysis include sequence homology analysis, differential expression gene screening, and functional annotation. Multi-dimensional data integration and statistical analysis technology include heterogeneous data mapping, correlation analysis, and pattern recognition. Population genetics analysis and phenotype association studies include genetic variant screening, phenotype data association, and risk prediction model establishment.

[0241] The DNA methylation analysis sub-module uses Bayesian network analysis of the patient's epigenetic data to analyze DNA methylation patterns, thereby understanding the DNA methylation status. By analyzing DNA methylation, DNA methylation analysis results are generated, which are very important for understanding gene expression and potential genetic changes.

[0242] The histone modification analysis sub-module uses mass spectrometry analysis and cross-immunoprecipitation techniques to analyze histone modification patterns. Histone modification plays a key role in gene regulation, so this analysis helps to understand gene function and regulatory mechanisms and generates histone modification analysis results.

[0243] The biomarker identification sub-module uses gene sequence alignment and expression profile analysis to identify biomarkers. Biomarkers are molecules related to diseases, and their identification helps to understand disease mechanisms. This process generates biomarker identification results.

[0244] The data fusion sub-module uses multi-dimensional data integration and statistical analysis techniques to integrate biological data from different data sources. This helps to build a more comprehensive biological picture and generates data fusion results, providing a basis for subsequent analysis.

[0245] Based on the data fusion results and treatment effect evaluation reports, the genetic risk assessment sub-module uses population genetics analysis and phenotype association studies to assess the genetic risk of patients. This helps to predict the genetic risk of diseases and generates genetic risk assessment reports.

[0246] Please refer to Figure 10 , the knowledge integration and decision support module includes a knowledge management sub-module, a data visualization sub-module, a decision recommendation generation sub-module, a medical knowledge base update sub-module, and a clinical application interface sub-module;

[0247] Based on the genetic risk assessment report, the knowledge management sub-module applies knowledge graph construction and semantic analysis techniques for data integration and analysis and generates knowledge management results;

[0248] Based on the knowledge management results, the data visualization sub-module uses interactive visualization and data exploration techniques to display data and analysis results and generates a data visualization report;

[0249] Based on the data visualization report and the comprehensive dataset, the decision recommendation generation sub-module uses expert systems and machine learning algorithms to propose treatment recommendations and generates decision-making schemes;

[0250] Based on the decision-making scheme and the deep pattern recognition results, the medical knowledge base update sub-module uses automated literature mining and data mining techniques to update the medical knowledge base and completes the update of the medical knowledge base;

[0251] The Clinical Application Interface Sub-module develops APIs and data standardization technologies based on the updated results of the medical knowledge base and the personalized treatment plan, and generates a clinical application interface report;

[0252] Knowledge graph construction and semantic analysis technologies include entity recognition, relationship extraction, and knowledge reasoning. Interactive visualization and data exploration technologies include chart generation, user interface design, and data interaction. Expert systems and machine learning algorithms include rule reasoning, pattern recognition, and predictive modeling. Automated literature mining and data mining technologies include literature screening, key information extraction, and knowledge update. APIs and data standardization technologies include interface design, data format unification, and system integration.

[0253] The Knowledge Management Sub-module applies knowledge graph construction and semantic analysis technologies based on the genetic risk assessment report to conduct data integration and analysis, thereby forming comprehensive medical knowledge management results. This includes key information extracted from genetic risk assessments and relevant information in the medical knowledge base.

[0254] The Data Visualization Sub-module uses interactive visualization and data exploration technologies to present data and analysis results in a user-friendly manner. This helps doctors and patients better understand complex medical information and generates a data visualization report.

[0255] Based on the data visualization report and the comprehensive dataset, the Decision Recommendation Generation Sub-module adopts expert systems and machine learning algorithms to propose personalized treatment recommendations and decision-making solutions. These recommendations are based on the patient's genetic risk and molecular biological characteristics and help doctors formulate precise treatment plans.

[0256] The Medical Knowledge Base Update Sub-module uses automated literature mining and data mining technologies to update the medical knowledge base based on the decision-making solution and the deep pattern recognition results. This ensures that the system always has the latest medical knowledge to support the generation of decision recommendations.

[0257] The Clinical Application Interface Sub-module develops APIs and data standardization technologies based on the updated results of the medical knowledge base and the personalized treatment plan to integrate the system's output into clinical applications. These interfaces can be used for integration with other clinical tools such as medical record systems.

[0258] The above are only the preferred embodiments of the present invention and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent clinical test result interpretation system, characterized by: The system includes a data acquisition module, a preliminary analysis module, a deep pattern recognition module, a disease prediction module, a personalized treatment module, a treatment effect monitoring module, an epigenetic analysis module, and a knowledge integration and decision support module; The data acquisition module uses data mining technology to collect and integrate data based on clinical test results and patient medical records to generate a comprehensive data set; The preliminary analysis module uses statistical analysis and time series analysis methods based on the comprehensive data set to perform data trend analysis and generate a preliminary analysis report; The deep pattern recognition module uses a deep learning algorithm to perform data pattern recognition based on the preliminary analysis report and generate a deep pattern recognition result; The disease prediction module uses a gradient boosting machine based on the deep pattern recognition results to predict the disease development trend and generate a disease prediction report; The personalized treatment module uses a personalized algorithm to design a treatment plan based on the disease prediction report and the patient's clinical data to generate a personalized treatment plan; The treatment effect monitoring module is based on a personalized treatment plan, tracks the patient's treatment response, uses real-time monitoring and data analysis methods to evaluate the treatment effect, and generates a treatment effect evaluation report; The epigenetic analysis module uses bioinformatics methods to conduct in-depth analysis based on the treatment effect evaluation report and the patient's epigenetic data to generate an epigenetic analysis report; The knowledge integration and decision support module is based on epigenetic analysis reports, comprehensive data sets, preliminary analysis reports, deep pattern recognition results, personalized treatment plans, and treatment effect evaluation reports. It uses knowledge management and decision support technologies to provide suggestions for clinical decision-making and generate decision support reports.

2. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The comprehensive data set includes clinical test parameters, patient medical history records and real-time physiological monitoring data. The deep pattern recognition results include potential health risk patterns, key biomarkers and predictive health indicators. The disease prediction report includes future health risk assessment, estimated disease development path and preventive intervention recommendations. The personalized treatment plan includes customized drug selection, treatment adjustment recommendations and patient treatment monitoring plans. The treatment effect evaluation report includes treatment response metrics, adverse reaction monitoring and treatment adjustment recommendations. The epigenetic analysis report includes DNA methylation patterns, genetic risk assessment and associated biomarker identification. The decision support report includes treatment strategy optimization recommendations, patient management strategies and inspiration for future research directions.

3. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The data acquisition module includes a data interface submodule, a medical record analysis submodule, a real-time data acquisition submodule, a data cleaning submodule, and a data standardization submodule; The data interface submodule uses data integration technology to integrate data based on clinical test results and patient medical records to generate original data sets; The medical record analysis submodule uses text analysis technology to extract information based on the original data set and generate patient health records; The real-time data acquisition submodule uses physiological signal processing technology to perform real-time monitoring based on the patient's health records and generate real-time monitoring data; The data cleaning submodule uses data purification technology to optimize data and generate purified data based on real-time monitoring data; The data standardization submodule generates a comprehensive data set based on the cleansed data and adopts data formatting technology; The data integration technology includes heterogeneous data source adaptation, ETL process and data warehouse construction; the text analysis technology specifically includes semantic analysis, keyword mining and context recognition; the physiological signal processing technology includes signal denoising, waveform analysis and real-time data stream encoding; the data purification technology specifically includes outlier removal, data normalization and duplicate data elimination; the data formatting technology specifically includes data mapping, data alignment and encoding conversion.

4. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The preliminary analysis module includes a statistical analysis submodule, a time series analysis submodule, a trend identification submodule, an anomaly detection submodule, and a correlation analysis submodule; The statistical analysis submodule uses statistical modeling technology based on the comprehensive data set to perform data feature analysis and generate statistical analysis results; The time series analysis submodule uses time series forecasting technology to perform trend analysis based on statistical analysis results and generate a time series analysis report; The trend identification submodule uses data trend mining technology based on the time series analysis report to perform trend identification and generate trend identification results; The anomaly detection submodule uses anomaly point recognition technology based on trend recognition results to perform anomaly analysis and generate anomaly detection reports; The correlation analysis submodule generates a preliminary analysis report based on the anomaly detection report using association rule analysis technology; The statistical modeling technology includes multivariate analysis, regression analysis and hypothesis testing, the time series forecasting technology includes autoregressive moving average model, seasonal decomposition and volatility modeling, the data trend mining technology includes linear trend analysis and nonlinear trend detection, the anomaly identification technology is specifically clustering-based anomaly detection, density-based outlier identification and time series anomaly detection, and the association rule analysis technology includes support and confidence calculation, frequent item set mining and rule generation.

5. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The deep pattern recognition module includes a feature extraction submodule, a pattern classification submodule, a deep learning training submodule, a model optimization submodule, and a result verification submodule; The feature extraction submodule extracts key features and generates a feature set based on the preliminary analysis report by using feature selection and dimensionality reduction techniques; The pattern classification submodule classifies data patterns based on feature sets and uses a supervised learning classification algorithm to generate pattern classification results; The deep learning training submodule uses a deep neural network to train the data pattern and generate a training model based on the pattern classification results; The model optimization submodule optimizes model performance parameters based on the training model by using hyperparameter adjustment and cross-validation to generate an optimized model; The result verification submodule is based on the optimization model, uses performance indicator evaluation to verify the model effect, performs result verification, and generates deep pattern recognition results; The feature selection and dimensionality reduction techniques include principal component analysis, linear discriminant analysis and autoencoder, the supervised learning classification algorithms are specifically logistic regression, random forest and gradient boosting decision tree, the deep neural network is specifically convolutional neural network, recurrent neural network, the hyperparameter adjustment and cross-validation include grid search, Bayesian optimization and K-fold cross-validation, and the performance indicator evaluation includes accuracy, recall rate and F1 score evaluation.

6. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The disease prediction module includes a disease trend analysis submodule, a risk assessment submodule, a prediction model training submodule, a prediction result verification submodule, and an early warning submodule; The disease trend analysis submodule uses statistical trend analysis based on the deep pattern recognition results to perform trend analysis and generate a trend analysis report; The risk assessment submodule uses a risk scoring model based on the trend analysis report to assess disease risk and generate a risk assessment report; The prediction model training submodule trains the disease prediction model based on the risk assessment report using a gradient boosting machine to generate a trained prediction model; The prediction result verification submodule verifies the prediction accuracy based on the trained prediction model and generates a prediction result verification report by using model evaluation technology; The early warning submodule provides early warning of diseases, conducts risk notification, and generates disease prediction reports based on the prediction result verification report and the real-time monitoring and alarm system; The statistical trend analysis includes moving average and exponential smoothing methods, the risk scoring model includes Cox proportional risk model and risk stratification method, the gradient boosting machine is specifically XGBoost, LightGBM and CatBoost, the model evaluation technology includes confusion matrix, ROC-AUC curve analysis, and the real-time monitoring and alarm system includes threshold trigger alarm and real-time data monitoring.

7. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The personalized treatment module includes a treatment plan design submodule, a patient data analysis submodule, a treatment plan optimization submodule, a treatment effect prediction submodule, and a patient feedback analysis submodule; The treatment plan design submodule uses a multi-factor decision tree algorithm to conduct a preliminary design of a personalized treatment plan based on the disease prediction report and the patient's clinical data, and conducts a preliminary evaluation of the plan to generate a draft personalized treatment plan; The patient data analysis submodule uses cluster analysis method to perform patient data segmentation and feature analysis based on the draft personalized treatment plan, and fine-tunes the plan to generate a patient data analysis report; The treatment plan optimization submodule uses a genetic algorithm to perform in-depth optimization of the treatment plan based on the patient data analysis report, and evaluates the optimization effect to generate an optimized personalized treatment plan; The treatment effect prediction submodule uses a Bayesian network prediction model based on the optimized personalized treatment plan to perform probability prediction of the treatment effect, analyze the prediction results, and generate a treatment effect prediction report; The patient feedback analysis submodule uses sentiment analysis technology based on the treatment effect prediction report to perform sentiment analysis of patient feedback, and conducts a comprehensive analysis of the feedback content to generate a comprehensive analysis report on patient feedback; The multi-factor decision tree algorithm includes decision tree construction based on disease severity classification, patient individual difference factor weight calculation and historical treatment response pattern analysis; the cluster analysis method includes K-means clustering and hierarchical cluster analysis; the genetic algorithm specifically includes gene coding optimization, fitness function design, crossover and mutation operations based on the patient's historical treatment response; the Bayesian network prediction model includes conditional probability table construction, network parameter learning based on historical data and probabilistic inference of treatment effect; the sentiment analysis technology specifically includes sentiment recognition algorithm in natural language processing.

8. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The treatment effect monitoring module includes a real-time monitoring submodule, an effect evaluation submodule, a data updating submodule, a feedback integration submodule, and an adjustment suggestion submodule; The real-time monitoring submodule uses biological signal monitoring technology based on the optimized personalized treatment plan to monitor the patient's treatment process in real time, and performs real-time data analysis to generate real-time health monitoring data; The effect evaluation submodule uses statistical analysis methods based on real-time health monitoring data to conduct quantitative evaluation of treatment effects, and conducts comprehensive analysis of the effects to generate a comprehensive evaluation report on treatment effects; The data update submodule uses database update technology to update and integrate patient treatment data based on the comprehensive evaluation report of treatment effects, and performs data consistency check to generate updated patient data; The feedback integration submodule uses data fusion technology to conduct in-depth integrated analysis of treatment feedback based on the updated patient data and the patient feedback comprehensive analysis report, and evaluates the integrated results to generate a treatment feedback integrated analysis report; The adjustment suggestion submodule formulates adjustment suggestions for the treatment plan based on the treatment feedback integration analysis report, adopts a decision support system, and performs effectiveness analysis of the suggestions to generate a treatment adjustment suggestion report; The bio-signal monitoring technology includes real-time acquisition of multi-parameter physiological signals, signal quality assessment, and health status analysis based on bioelectric signals. The statistical analysis method specifically includes time series analysis, survival analysis, and construction of a multivariate regression model based on treatment effects. The database update technology includes a data synchronization mechanism in a cloud database, a dynamic data update algorithm, and data consistency and integrity verification. The data fusion technology specifically includes multi-source data integration, comprehensive analysis based on historical and current data, and dimensionality reduction and fusion strategies for data dimensions. The decision support system includes a rule engine in an expert system, pattern recognition based on artificial intelligence, and simulation of treatment plan adjustment.

9. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The epigenetic analysis module includes a DNA methylation analysis submodule, a histone modification analysis submodule, a biomarker identification submodule, a data fusion submodule, and a genetic risk assessment submodule; The DNA methylation analysis submodule uses a Bayesian network to analyze DNA methylation patterns based on patient epigenetic data and generates DNA methylation analysis results; The histone modification analysis submodule analyzes the histone modification pattern based on the DNA methylation analysis results using mass spectrometry analysis and cross-immunoprecipitation technology, and generates histone modification analysis results; The biomarker identification submodule identifies target biomarkers based on the histone modification analysis results using gene sequence alignment and expression profile analysis, and generates biomarker identification results; The data fusion submodule integrates multi-source biological data based on the biomarker identification results using multi-dimensional data integration and statistical analysis techniques, and generates data fusion results; The genetic risk assessment submodule uses population genetics analysis and phenotypic association studies based on data fusion results and treatment effect evaluation reports to conduct risk assessment and generate a genetic risk assessment report; The Bayesian network includes structure learning, parameter learning and inference algorithms, the mass spectrometry analysis and cross-immunoprecipitation technology includes peptide separation, quality detection and protein interaction network analysis, the gene sequence alignment and expression spectrum analysis include sequence homology analysis, differentially expressed gene screening and functional annotation, the multidimensional data integration and statistical analysis technology include heterogeneous data mapping, association analysis and pattern recognition, and the population genetics analysis and phenotypic association research include genetic variation screening, phenotypic data association and risk prediction model establishment.

10. The intelligent clinical test result interpretation system according to claim 1, characterized in that: The knowledge integration and decision support module includes a knowledge management submodule, a data visualization submodule, a decision suggestion generation submodule, a medical knowledge base update submodule, and a clinical application interface submodule; The knowledge management submodule is based on the genetic risk assessment report, applies knowledge graph construction and semantic analysis technology, performs data integration and analysis, and generates knowledge management results; The data visualization submodule uses interactive visualization and data exploration technology based on knowledge management results to display data and analysis results and generate data visualization reports; The decision suggestion generation submodule uses expert systems and machine learning algorithms based on data visualization reports and comprehensive data sets to make treatment suggestions and generate decision plans; The medical knowledge base updating submodule updates the medical knowledge base based on the decision-making scheme and the deep pattern recognition results using automated literature mining and data mining techniques, and completes the medical knowledge base update; The clinical application interface submodule develops API and data standardization technology based on the medical knowledge base update results and personalized treatment plans, and generates clinical application interface reports; The knowledge graph construction and semantic analysis technology includes entity recognition, relationship extraction and knowledge reasoning, the interactive visualization and data exploration technology includes chart generation, user interface design and data interaction, the expert system and machine learning algorithm includes rule reasoning, pattern recognition and predictive modeling, the automated literature mining and data mining technology includes literature screening, key information extraction and knowledge updating, and the API and data standardization technology includes interface design, data format unification and system integration.

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