A test result evaluation system

Abstract

The application discloses a kind of test result evaluation systems in the technical field of medical examination evaluation, system includes physiological dynamic identification module, critical state interpretation module, process efficiency optimization module, health trend prediction module, test accuracy improvement module, result interpretation optimization module, potential pathology mining module, health evaluation comprehensive module.In the present application, by introducing nonlinear dynamics model, fuzzy logic, machine learning algorithm and other technologies, the depth and breadth of medical test evaluation are significantly improved.First, the application of physiological dynamic identification module makes the analysis of time series data more accurate, and can reveal subtle changes in physiological behavior, thereby improving the ability of early disease identification.The introduction of fuzzy logic shows higher flexibility and accuracy in dealing with the uncertainty and fuzziness of test results, especially in the judgment of boundary values and critical states, providing more delicate support for medical decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of medical test evaluation, and in particular to a test result evaluation system. Background Art

[0002] The field of medical laboratory evaluation technology focuses on the development and application of various tools, methods, and systems for accurate and efficient analysis and interpretation of medical test results. This area encompasses a wide range of aspects, from biomarker detection, disease diagnosis, efficacy evaluation, to patient health monitoring. Technological approaches include, but are not limited to, biochemical analysis, molecular biology techniques, data processing software, and artificial intelligence algorithms. These technological advances enable medical laboratory evaluation to not only provide more accurate diagnostic information but also help physicians formulate more precise treatment recommendations.

[0003] A test result evaluation system utilizes computer technology, artificial intelligence, and statistical methods to analyze and interpret medical test results. Its goal is to improve the accuracy of test results and the effectiveness of interpretation, reduce human error, and expedite diagnosis, thereby providing patients with more accurate and timely medical services. Through in-depth analysis of test data, this system can reveal potential health issues and assist physicians in making more informed decisions in complex clinical situations. Ultimately, the system aims to improve patient treatment outcomes and health by enhancing the quality and efficiency of medical testing.

[0004] While existing technologies have achieved significant success in the field of medical laboratory assessment, they suffer from insufficient analytical accuracy when dealing with the dynamic changes of complex physiological and pathological processes. In particular, traditional technologies struggle to capture subtle system dynamics when analyzing time series data and simulating physiological behavior, resulting in limited ability to identify potential pathological conditions early on. Furthermore, existing systems are limited in their interpretive capabilities when dealing with the uncertainty and ambiguity in medical test results, making it difficult to provide detailed and accurate health status assessments. This is particularly true when analyzing boundary values ​​and critical states, where the lack of flexibility and fine-grained interpretation impacts the accuracy of medical decision-making. Regarding laboratory process optimization, traditional technologies lack the ability to monitor and predictively adjust the laboratory process in real time, making it difficult to promptly identify and resolve bottlenecks and delays, impacting laboratory efficiency and service response time. When analyzing and recognizing patterns in test results, traditional technologies fail to fully utilize statistical principles and pattern recognition techniques, limiting their ability to identify subtle differences and their changing patterns, and reducing the accuracy of predicting potential health issues and disease trends. Finally, in terms of test result variability analysis tools, there is a lack of advanced statistical models to deeply analyze the source of result variation, making it difficult to provide laboratories with effective improvement strategies, which in turn affects the stability of the testing process and the consistency of the results.

[0005] Based on this, the present invention designs a test result evaluation system to solve the above problems. Summary of the Invention

[0006] The present invention aims to provide a test result evaluation system to address the problem raised in the aforementioned background art: while existing technologies have achieved significant success in the field of medical test evaluation, they suffer from insufficient analytical accuracy when dealing with the dynamic changes of complex physiological and pathological processes. In particular, when it comes to in-depth analysis of time series data and physiological behavior simulation, traditional technologies struggle to capture subtle system dynamics, resulting in limited ability to identify potential pathological conditions early on. Furthermore, when dealing with the uncertainty and ambiguity in medical test results, existing systems are limited in their interpretive capabilities, making it difficult to provide detailed and accurate health status assessments. In particular, the lack of flexibility and fine-grained interpretation in boundary value and critical state analysis compromises the accuracy of medical decision-making. Regarding test process optimization, traditional technologies lack the ability to monitor and predictively adjust test processes in real time, making it difficult to promptly identify and resolve bottlenecks and delays in the process, impacting test efficiency and service response time. When it comes to differential analysis and pattern recognition of test results, traditional technologies fail to fully utilize statistical principles and pattern recognition techniques, limiting their ability to identify subtle differences and their changing patterns, and weakening the accuracy of predicting potential health issues and disease trends. Finally, in terms of test result variability analysis tools, there is a lack of advanced statistical models to deeply analyze the source of result variation, making it difficult to provide laboratories with effective improvement strategies, which in turn affects the stability of the testing process and the consistency of the results.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a test result evaluation system, comprising a physiological dynamic recognition module, a critical state interpretation module, a process efficiency optimization module, a health trend prediction module, a test accuracy improvement module, a result interpretation optimization module, a potential pathology mining module, and a comprehensive health assessment module;

[0008] The physiological dynamic identification module simulates physiological behavior based on time series data of medical examinations using nonlinear dynamic models and attractor analysis, analyzes the impact of parameter changes on behavior through bifurcation theory, identifies steady state, periodicity or chaotic state, and generates physiological dynamic diagnosis results;

[0009] The critical state interpretation module uses fuzzy logic to construct a rule base and membership function based on the physiological dynamic diagnosis results, analyzes and infers the boundary values ​​and critical states in the test results, and generates critical state analysis results;

[0010] The process efficiency optimization module uses random forest and neural networks based on the critical state analysis results to analyze and inspect process data, identify potential bottlenecks and delays in the process, adjust resource allocation and process settings based on the prediction results, and generate process optimization suggestions;

[0011] The health trend prediction module, based on process optimization recommendations, uses time series analysis methods and clustering-based anomaly detection algorithms to analyze the changing trends of test results over time and identify abnormal patterns, predict changes in health status and potential disease risks, and generate trend prediction results;

[0012] The test accuracy improvement module uses partial least squares regression and generalized additive models based on trend prediction results to analyze the variability of test results and identify the causes of inconsistent results, including analysis of the sources of variability and quantification of result fluctuations, and generates improvement measures.

[0013] The result interpretation optimization module uses a conditional random field model based on improvement measures, integrates custom feature extraction and prior knowledge, interprets medical test results, including interaction analysis between features and model training, identifies and evaluates key information, and generates refined interpretation results;

[0014] The potential pathology mining module uses cluster analysis and multivariate statistical analysis based on the refined interpretation results to identify patient groups with similar patterns from medical test results, analyze the groups and reveal potential health status or disease classifications, and generate potential pathology analysis results;

[0015] The comprehensive health assessment module uses a comprehensive assessment algorithm based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measure suggestions, detailed interpretation results, and potential pathology analysis results to evaluate and comprehensively judge the patient's health status, perform comprehensive risk grading, and generate comprehensive health assessment results.

[0016] Preferably, the physiological dynamic diagnosis result includes a stability parameter, a periodic fluctuation frequency, and a chaotic dynamic characteristic value, the critical state analysis result includes a fuzzy logic judgment level, a boundary value sensitivity index, and a state transition early warning signal, the process optimization suggestion includes a key equipment scheduling priority, a detection step simplification plan, and an emergency response mechanism adjustment, the trend prediction result includes a health state evolution trend graph, an abnormal health indicator list, and a risk disease early warning identification, the improvement measure suggestion includes a test precision optimization, a data processing algorithm update, and an experimental operation standardization process, the refined interpretation result includes a key biomarker analysis, a disease correlation score, and a treatment response potential indicator, the potential pathology analysis result includes a non-emergent pathology state signal, a population health trend deviation, and a predictive disease classification atlas, and the comprehensive health assessment result includes an individual health comprehensive score, a potential health risk level, and a health management suggestion.

[0017] Preferably, the physiological dynamic recognition module includes a steady state analysis submodule, a periodicity recognition submodule, and a chaotic state judgment submodule.

[0018] The steady state analysis submodule is constructed based on time series data of medical tests by using a nonlinear dynamics model, numerical integration is performed by using an integrate module in a SciPy library of Python, parameters include a time step and an initial condition, dynamic properties of physiological activities are simulated, whether physiological activities reach a steady state is identified by using an attractor and a bifurcation theory analysis, and a graphical display of parameter changes is performed by using a Matplotlib library, and a steady state characteristic analysis result is generated.

[0019] The periodicity recognition submodule is based on the steady state characteristic analysis result, applies Fourier transform to detect periodic fluctuations again, performs transformation by using an fft function of a NumPy library, sets parameters including a sampling rate and a data volume, finds out a repeated pattern or a periodic fluctuation in a time series, selects periodic properties of physiological activities by spectrum analysis, and generates a periodic fluctuation detection result.

[0020] The chaotic state judgment submodule is based on the periodic fluctuation detection result, uses Lyapunov exponent to evaluate a chaotic state, calculates Lyapunov exponent by using a Nolds library of Python, parameters include an embedding dimension and a data sequence, evaluates sensitivity of a physiological process dynamic to an initial condition and unpredictability thereof, determines whether physiological activities exhibit chaotic characteristics by using a calculation result of Lyapunov exponent, and generates a physiological dynamic diagnosis result.

[0021] Preferably, the critical state interpretation module includes a fuzzy logic analysis submodule, a risk assessment submodule, and a critical state judgment submodule.

[0022] The fuzzy logic analysis submodule performs fuzzy logic analysis based on the physiological dynamic diagnosis results. It defines the membership function of the input variables through the Scikit-Fuzzy library in Python. The membership function is set to Gaussian distribution according to the distribution of medical data, and a rule base is constructed. The input data is processed through the fuzzy inference engine, and the clear physiological parameters are converted into fuzzy values ​​to generate fuzzy logic analysis results.

[0023] The risk assessment submodule uses a decision tree algorithm based on the results of fuzzy logic analysis and is executed through the DecisionTreeClassifier function in the Scikit-Learn library. The decision tree is constructed based on the fuzzy values, with a maximum depth of 5 layers to avoid overfitting. Information gain is used as the node splitting criterion to convert the fuzzy values ​​into risk levels. Based on the fuzzy classification provided by fuzzy logic, the risk under each physiological state is quantitatively assessed to generate a risk assessment result for the individual patient.

[0024] The critical state determination submodule uses the PyKnow library to build an expert rule engine based on the risk assessment results of individual patients, integrates the scattered risk levels into critical state determinations, and re-evaluates the patient's current health status to generate critical state analysis results.

[0025] Preferably, the process efficiency optimization module includes a bottleneck identification submodule, a delay prediction submodule, and a resource allocation submodule;

[0026] The bottleneck identification submodule uses the Random Forest algorithm based on the critical state analysis results. It uses the RandomForestClassifier of the Scikit-Learn library in the Python environment, with configuration parameters including n_estimators set to 100 and max_depth set to None. It analyzes the data in the inspection process, identifies potential bottlenecks, and locates the links with abnormal efficiency in the process to generate bottleneck identification analysis results.

[0027] The delay prediction submodule performs delay prediction based on the bottleneck identification analysis results. It uses a neural network algorithm, through the TensorFlow and Keras libraries, to build a multi-stage feature including an input layer matching verification process, uses the ReLU activation function in the hidden layer, and the sigmoid function in the output layer. The optimizer is set to Adam to optimize the model parameters, and the loss function uses binary_crossentropy. It predicts the delay that occurs at each stage based on the bottleneck analysis and generates a delay prediction analysis result.

[0028] The resource allocation submodule performs resource allocation based on the delay prediction analysis results. It adopts linear programming method and defines the optimization problem objective as minimizing the total delay time through the PuLP library. The constraints include that resource allocation should not exceed the predetermined budget and resource type restrictions. It also adjusts the resource allocation strategy and process settings and generates process optimization suggestions.

[0029] Preferably, the health trend prediction module includes a time change analysis submodule, an outlier identification submodule, and a trend prediction submodule;

[0030] The time change analysis submodule uses Python's pandas and statsmodels libraries to process time series data based on process optimization suggestions, constructs an autoregressive integrated moving average model, defines the model's autoregressive term, difference order, and moving average term by setting the order parameter to a specified (p, d, q) value, fits the model using the fit method, plots the predicted trend of the time series using the plot_predict method, and generates a time series change trend graph;

[0031] The outlier identification submodule uses the Python scikit-learn library to implement the K-means algorithm based on the time series change trend graph, defines the number of clusters by setting the n_clusters parameter, uses the fit_predict method to perform cluster analysis on the data, uses a distance metric to identify the distance between each data point and its nearest cluster center, identifies data points with a distance greater than a specified threshold as outliers, and generates an abnormal pattern recognition result;

[0032] The trend prediction submodule performs trend prediction based on the abnormal pattern recognition results, uses Python's SciPy library to perform exponential smoothing, sets seasonal adjustment parameters and smoothing parameters through the ExponentialSmoothing class, fits the data using the fit method, applies the forecast method to predict future values, makes adjustments based on the impact of abnormal values, and generates trend prediction results.

[0033] Preferably, the inspection accuracy improvement module includes a variability analysis submodule, an influencing factor identification submodule, and an improvement measure proposal submodule;

[0034] The variability analysis submodule performs partial least squares regression analysis based on the trend prediction results. It uses the PLSRegression class in the Python scikit-learn library and sets the number of components parameter n_components to 5 to perform fitting analysis on the data. By calculating the covariance between each variable and the response variable, it identifies the factors that contribute most to the variability of the test results, predicts and analyzes the data set, and generates variability source analysis results.

[0035] The influencing factor identification submodule is based on the results of the variability source analysis and applies generalized additive model analysis. It uses the Python pyGAM library and operates through the LinearGAM or GAM class. The smoothing parameter splines is set to 20 and the n_splines parameter is set to automatic selection. The module analyzes the nonlinear relationship between the variability of the test results and the potential influencing factors, including selecting the response variable and the explanatory variables, and selecting the importance of the explanatory variables through statistical methods to generate the influencing factor identification results.

[0036] The improvement measure proposal submodule reviews the inspection process and data processing methods based on the results of variability source analysis and influencing factor identification, identifies improvement directions, including adjusting the experimental design, optimizing the data processing algorithm, formulating an improvement plan, including adjusting the experimental operation standardization process, and using data analysis technology to improve the consistency of results, and generates improvement measure suggestions.

[0037] Preferably, the result interpretation optimization module includes a feature analysis optimization submodule, a knowledge integration submodule, and an interpretation accuracy improvement submodule;

[0038] The feature analysis and optimization submodule performs feature analysis and optimization of the conditional random field model based on the improvement measures suggested. Using the Python sklearn_crfsuite library, the model regularization degree is adjusted by setting the parameters of the CRF class algorithm = lbfgs, c1 = 0.1 and c2 = 0.1. The model is trained using a training dataset, and the fit method is used to analyze the features and their interactions in the medical test results, extract key information related to the interpretation of the results, and generate feature analysis results.

[0039] The knowledge integration submodule integrates prior knowledge based on the feature analysis results, adds prior knowledge in the medical field to the conditional random field model by setting a custom function, and uses the information to optimize the recognition ability of the model to generate knowledge integration results;

[0040] The interpretation accuracy optimization submodule further optimizes the interpretation accuracy based on the knowledge integration results, optimizes the parameters of the conditional random field model, sets max_iterations=1000 to enable the model to fully learn, applies the predict method to interpret the new test results, and generates refined interpretation results.

[0041] Preferably, the potential pathology mining module includes a cluster identification submodule, a health status classification submodule, and a pathology pattern analysis submodule;

[0042] The cluster identification submodule performs cluster analysis based on the refined interpretation results. It uses the K-means algorithm and executes the KMeans class through the Python scikit-learn library. It sets the n_clusters parameter to estimate the number of groups based on data characteristics and uses the fit_predict method to cluster the medical test result data, identify patient groups with similar patterns, and generate cluster grouping results.

[0043] The health status classification submodule is based on the clustering grouping results, and uses the DecisionTreeClassifier class of the scikit-learn library to set the max_depth parameter to control the maximum depth of the tree to prevent overfitting, and performs health status classification on the clustering results to generate health status classification results;

[0044] The pathology pattern analysis submodule performs multivariate statistical analysis based on the health status classification results, including principal component analysis and linear discriminant analysis, using the scikit-learn library. The n_components parameter is set to select the number of principal components, revealing pathology patterns among multiple health status classifications and generating potential pathology analysis results.

[0045] Preferably, the comprehensive health assessment module includes a comprehensive risk assessment submodule, a trend comprehensive analysis submodule, and a pathology comprehensive judgment submodule;

[0046] The comprehensive risk assessment submodule uses a comprehensive assessment algorithm to assess the patient's health status based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measures suggestions, detailed interpretation results, and potential pathology analysis results. It combines multi-source data through a comprehensive weighted scoring model, with parameters including the influence weight of each data source, to classify the patient's health risk and generate a comprehensive risk rating result;

[0047] The trend comprehensive analysis submodule performs time series analysis and trend forecasting based on the comprehensive risk rating results, and uses the autoregressive integrated moving average model to predict long-term health trends. The parameter settings reflect the changing patterns of historical data, including the difference level and the number of autoregressive terms, to predict future changes in health status and generate health trend forecast analysis results;

[0048] The pathology comprehensive judgment submodule performs a comprehensive pathology judgment based on the health trend prediction analysis results and the potential pathology analysis results, and uses logistic regression analysis to evaluate the development probability of a specified pathological state. Parameters include regression coefficients, which are adjusted based on the patient's health data and potential pathology characteristics to provide advice for medical decision-making and generate comprehensive health assessment results.

[0049] Compared with existing technologies, the present invention significantly enhances the depth and breadth of medical laboratory assessments by incorporating technologies such as nonlinear dynamic models, fuzzy logic, and machine learning algorithms. First, the application of the physiological dynamics recognition module enables more precise analysis of time series data, revealing subtle changes in physiological behavior and thus improving the ability to identify diseases early. The introduction of fuzzy logic provides greater flexibility and precision in handling the uncertainty and ambiguity of test results, particularly in determining boundary values ​​and critical states, providing more nuanced support for medical decision-making. Furthermore, the implementation of the process efficiency optimization module enables timely identification and resolution of bottlenecks and delays in the testing process, significantly improving testing efficiency and response speed. The combination of the health trend prediction module and the potential pathology mining module provides powerful data support for identifying changing trends in a patient's health status and potential disease risks, enhancing the foresight of disease prevention and health management. Finally, the application of the test accuracy improvement module and the result interpretation optimization module not only optimizes the testing process and reduces result variability, but also improves the accuracy and efficiency of test result interpretation, providing laboratories with improvement strategies and fundamentally improving the stability of the testing process and the consistency of results. In summary, the present invention has brought about comprehensive improvements in the field of medical testing and evaluation, especially showing significant effects in improving diagnostic accuracy, optimizing testing processes, enhancing predictive capabilities and improving health assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A module diagram of a test result evaluation system is proposed for the present invention;

[0052] Figure 2 A system framework diagram of a test result evaluation system is proposed for the present invention;

[0053] Figure 3 A schematic diagram of a physiological dynamics recognition module in a test result evaluation system proposed by the present invention;

[0054] Figure 4 A schematic diagram of a critical state interpretation module in a test result evaluation system proposed by the present invention;

[0055] Figure 5 A schematic diagram of a process efficiency optimization module in an inspection result evaluation system proposed by the present invention;

[0056] Figure 6 A schematic diagram of a health trend prediction module in a test result evaluation system proposed by the present invention;

[0057] Figure 7 A schematic diagram of a test accuracy improvement module in a test result evaluation system proposed by the present invention;

[0058] Figure 8 A schematic diagram of a result interpretation optimization module in a test result evaluation system proposed by the present invention;

[0059] Figure 9 A schematic diagram of a potential pathology mining module in a test result evaluation system proposed by the present invention;

[0060] Figure 10 The present invention proposes a schematic diagram of a health assessment comprehensive module in a test result evaluation system. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] See also Figure 1 , the present invention provides a technical solution: a test result evaluation system, the system includes a physiological dynamic recognition module, a critical state interpretation module, a process efficiency optimization module, a health trend prediction module, a test accuracy improvement module, a result interpretation optimization module, a potential pathology mining module, and a health assessment comprehensive module;

[0063] The physiological dynamic recognition module simulates physiological behavior based on time series data of medical tests using a nonlinear dynamic model and attractor analysis, analyzes the impact of parameter changes on behavior through bifurcation theory, identifies steady state, periodic or chaotic state, and generates physiological dynamic diagnosis results.

[0064] The critical state interpretation module analyzes and reasons the boundary values and critical states in the test results based on the physiological dynamic diagnosis results, constructs a rule base and membership function using fuzzy logic methods, and generates critical state analysis results.

[0065] The process efficiency optimization module analyzes test process data to identify potential bottlenecks and delays in the process based on critical state analysis results using random forest and neural network, adjusts resource allocation and process settings according to prediction results, and generates process optimization suggestions.

[0066] The health trend prediction module analyzes the change trend of test results over time and identifies abnormal patterns based on process optimization suggestions using time series analysis methods and clustering-based anomaly detection algorithms, predicts changes in health status and potential disease risks, and generates trend prediction results.

[0067] The test accuracy improvement module analyzes the variability of test results and identifies the reasons for inconsistent results, including variability source analysis and result fluctuation quantification, based on trend prediction results using partial least squares regression and generalized additive model, and generates improvement measures suggestions.

[0068] The result interpretation optimization module integrates custom feature extraction and prior knowledge to interpret medical test results based on improvement measures suggestions using conditional random field model, including interaction analysis between features and model training, identifies and evaluates key information, and generates refined interpretation results.

[0069] The potential pathology mining module identifies patient groups with similar patterns from medical test results based on refined interpretation results using clustering analysis and multivariate statistical analysis, analyzes the groups and reveals potential health status or disease classification, and generates potential pathology analysis results.

[0070] The health assessment synthesis module evaluates and comprehensively judges the patient's health status and performs comprehensive risk grading based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measures suggestions, refined interpretation results, and potential pathology analysis results using comprehensive evaluation algorithms, and generates comprehensive health assessment results.

[0071] The physiological dynamic diagnosis results include stability parameters, periodic fluctuation frequencies, and chaotic dynamic characteristic values. The critical state analysis results include fuzzy logic judgment levels, boundary value sensitivity indexes, and state transition early warning signals. The process optimization suggestions include key equipment scheduling priorities, detection step simplification plans, and emergency response mechanism adjustments. The trend prediction results include health status evolution trend graphs, abnormal health indicator lists, and risk disease early warning identifiers. The improvement measure suggestions include test precision optimization, data processing algorithm updates, and experimental operation standardization processes. The refined interpretation results include key biomarker analysis, disease correlation scores, and treatment response potential indicators. The potential pathology analysis results include non-emergent pathology state signals, group health trend deviations, and predictive disease classification maps. The comprehensive health assessment results include individual health comprehensive scores, potential health risk levels, and health management suggestions.

[0072] In the physiological dynamic recognition module, the time series data of medical tests are processed by using a nonlinear dynamic model. In this process, numerical integration techniques are used to simulate the dynamic behavior of physiological systems, and attractor analysis and bifurcation theory are used to analyze the sensitivity of parameter changes. Specifically, numerical integration techniques such as the Runge-Kutta method are used to process time series data, and attractor theory is used to identify the stable state, periodic fluctuation or chaotic state of the physiological system. Further, bifurcation theory is used to analyze the behavior changes of the physiological system under parameter changes, thereby identifying the transition point of the physiological state. This series of operations can generate physiological dynamic diagnosis results including system stability parameters, periodic fluctuation frequencies, and chaotic dynamic characteristic values, providing a scientific basis for subsequent critical state judgment and health trend prediction.

[0073] In the critical state interpretation module, based on the physiological dynamic diagnosis results, a fuzzy logic method is used to construct a rule base and membership functions to analyze and reason the boundary values and critical states in the test results. By defining the fuzzy sets of each test indicator and the corresponding membership functions, the fuzzy logic engine performs fuzzy processing on the physiological parameters based on the constructed rule base, and then performs reasoning analysis on the boundary values and critical states to generate fuzzy logic judgment levels, boundary value sensitivity indexes, and state transition early warning signals. These operations can provide more detailed and flexible health status assessment for doctors, and help to improve the accuracy of clinical decision-making.

[0074] In the process efficiency optimization module, random forest and neural network algorithms are used to analyze test process data and identify potential bottlenecks and delays. The random forest algorithm identifies key factors affecting process efficiency by analyzing various metrics within the test process, such as sample processing time and equipment utilization efficiency. The neural network algorithm, on the other hand, uses this data to predict process delays. Based on these predictions, linear programming is used to adjust resource allocation and process settings, generating process optimization recommendations. These recommendations include prioritizing the scheduling of key equipment, simplifying testing procedures, and adjusting emergency response mechanisms. This series of operations has significantly improved the efficiency and responsiveness of the test process, reducing patient wait times.

[0075] In the health trend prediction module, based on process optimization recommendations, time series analysis methods and clustering-based anomaly detection algorithms are used to analyze the changing trends of test results over time. Time series analysis methods such as the Autoregressive Integrated Moving Average (ARIMA) model are used to predict long-term health trends, while clustering algorithms are used to identify abnormal health indicators, revealing sudden changes in health status or potential risks. Through these analyses, health status evolution trend charts, lists of abnormal health indicators, and risk disease warning signs are generated, providing data support for individual health management and preventive care.

[0076] In the test accuracy improvement module, based on trend prediction results, partial least squares regression and generalized additive models are used to analyze test result variability and identify the causes of inconsistent results. Partial least squares regression is used to analyze the complex relationship between test indicators and health status and identify sources of variability; while generalized additive models are used to quantify test result fluctuations and propose improvement measures such as optimizing test accuracy, updating data processing algorithms, and standardizing experimental procedures. These analyses and recommendations help improve the accuracy and consistency of test results and reduce misdiagnosis rates.

[0077] In the result interpretation optimization module, based on recommended improvement measures, a conditional random field model is used to integrate customized feature extraction and prior knowledge to provide a refined interpretation of medical test results. By analyzing features and their interactions within the test results, the conditional random field model identifies and evaluates key information, generating refined interpretations including key biomarker analysis, disease association scores, and indicators of potential treatment response. This process not only improves the accuracy of result interpretation but also provides physicians with more comprehensive and in-depth diagnostic information.

[0078] In the potential pathology mining module, based on the refined interpretation results, cluster analysis and multivariate statistical analysis are used to identify patient groups with similar patterns from medical test results and reveal potential health status or disease classification. Cluster analysis reveals specific health conditions or disease risks by grouping patients with similar patterns; multivariate statistical analysis further analyzes the commonalities and differences of these groups to generate non- apparent pathological state signals, group health trend deviations, and predictive disease classification maps. These analysis results provide important basis for precision medicine and group health management.

[0079] In the health assessment synthesis module, the analysis results of all the above modules are integrated, a comprehensive assessment algorithm is used to comprehensively assess and judge the health status of the patient, and a comprehensive risk classification is performed. Through comprehensive weighted scoring model calculation, physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions and other multi-source data are integrated to classify the health risk of the patient, and comprehensive health assessment results including individual health comprehensive score, potential health risk level and health management suggestions are generated. The comprehensive assessment provides health management suggestions for the patient, which helps to improve the health level and prevent diseases.

[0080] Please refer to Figure 2 and Figure 3 , the physiological dynamic recognition module includes a steady state analysis submodule, a periodicity recognition submodule, and a chaotic state judgment submodule;

[0081] The steady state analysis submodule is based on time series data of medical tests, uses a nonlinear dynamics model for construction, uses the integrate module in the SciPy library of Python for numerical integration, parameters include time step and initial condition, simulates the dynamic properties of physiological activities, analyzes through attractor and bifurcation theory, uses the Matplotlib library for graphical display of parameter changes, identifies whether the physiological activity has reached a stable state, and generates a steady state feature analysis result;

[0082] The periodicity recognition submodule is based on the steady state feature analysis result, applies Fourier transform to detect periodic fluctuations again, executes the transform through the fft function of the NumPy library, sets parameters including sampling rate and data volume, finds out repeating patterns or periodic fluctuations in time series, selects the periodic properties of physiological activities through spectral analysis, and generates a periodic fluctuation detection result;

[0083] The chaotic state judgment submodule is based on the periodic fluctuation detection result, uses Lyapunov exponent to evaluate chaotic state, uses the Nolds library of Python to calculate Lyapunov exponent, parameters include embedding dimension and data sequence, evaluates the sensitivity of physiological process dynamics to initial conditions and its unpredictability, determines whether the physiological activity exhibits chaotic characteristics through the calculation result of Lyapunov exponent, and generates a physiological dynamic diagnosis result.

[0084] In the steady-state analysis submodule, time series data from medical examinations are analyzed using a nonlinear dynamic model. The data is formatted as a time series, recording continuous observations of physiological activity. Numerical integration is performed using the integrate module in Python's SciPy library, where parameter settings include the time step and initial conditions, which are used to simulate the dynamic properties of physiological systems. Through analysis using attractor and bifurcation theory, the system can identify whether physiological activity has reached a stable state, is experiencing periodic fluctuations, or is in a chaotic state. This process is visualized using the Matplotlib library, making parameter changes and physiological state changes intuitive. Ultimately, this submodule generates steady-state characteristic analysis results, detailing the stability parameters of physiological dynamics and providing foundational data for subsequent critical state interpretation.

[0085] In the periodicity identification submodule, Fourier transforms are further employed to detect periodic fluctuations based on the results of the steady-state feature analysis. This transformation is performed using the fast Fourier transform (FFT) function of the NumPy library. Important parameter settings include the sampling rate and data size, which facilitate the identification of repetitive patterns or periodic fluctuations in time series data. Spectral analysis is then used to determine the periodic properties of physiological activity, including the frequency and intensity of the periodic fluctuations. Through these analyses, the submodule generates periodic fluctuation detection results that clearly indicate the presence of periodic characteristics in physiological activity, which is important for understanding the regularity of physiological processes and predicting future trends.

[0086] In the chaos state assessment submodule, the Lyapunov exponent is used to assess the chaotic state of physiological processes based on the results of periodic fluctuation detection. The Lyapunov exponent is calculated using the Python Nolds library. Key parameter settings include the embedding dimension and data sequence length, which help assess the sensitivity of physiological processes to initial conditions and their degree of unpredictability. The calculated Lyapunov exponent helps determine whether physiological activities exhibit chaotic characteristics, providing an accurate basis for identifying the nonlinear dynamics of physiological systems. Analysis through this submodule generates physiological dynamic diagnostic results that not only identify chaotic states but also provide a scientific basis for further health assessment and disease prevention.

[0087] Consider a scenario where a test result evaluation system analyzes electrocardiogram (ECG) data from a group of heart patients to identify whether their heart rhythm is steady, periodic, or chaotic, thereby assessing the patient's heart health. The ECG data item consists of a time series of heartbeats per minute (BPM). The simulated values ​​are as follows: over a 5-minute period, the heartbeats are 72, 75, 73, 70, 68, 74, 77, 75, 76, 72, 78, 81, and 79. The steady-state analysis submodule uses a nonlinear dynamic model to analyze this data. Numerical integration and attractor analysis determine heartbeat stability parameters, such as a stable heartbeat range of 70-80 beats per minute. The periodicity identification submodule applies a Fourier transform to identify periodic fluctuations in the heartbeat data, such as a BPM fluctuation frequency of 1 beat per minute. The chaotic state determination submodule calculates the Lyapunov exponent to assess the chaotic characteristics of the data. A positive Lyapunov exponent indicates a chaotic heart rhythm, suggesting a risk of cardiac instability. Through this series of analyses, the generated heart health assessment report describes in detail the dynamic characteristics of the patient's heart rhythm and health risk level, providing a scientific basis for clinical diagnosis and treatment.

[0088] See also Figure 2 and Figure 4 ,The critical state interpretation module includes a fuzzy logic analysis submodule, a risk assessment submodule, and a critical state determination submodule;

[0089] The fuzzy logic analysis submodule performs fuzzy logic analysis based on the physiological dynamic diagnosis results. Using the Scikit-Fuzzy library in Python, it defines the membership function of the input variables. The membership function is set to Gaussian distribution based on the distribution of medical data, and a rule library is constructed. The fuzzy inference engine processes the input data, converts clear physiological parameters into fuzzy values, and generates fuzzy logic analysis results.

[0090] The risk assessment submodule uses a decision tree algorithm based on fuzzy logic analysis results, executed through the DecisionTreeClassifier function in the Scikit-Learn library. The decision tree is constructed based on fuzzy values, with a maximum depth of 5 layers to avoid overfitting. Information gain is used as the node splitting criterion to convert fuzzy values ​​into risk levels. Based on the fuzzy classification provided by fuzzy logic, the risk is quantitatively assessed for each physiological state, generating individual patient risk assessment results.

[0091] The critical state determination submodule uses the PyKnow library to build an expert rule engine based on the risk assessment results of individual patients, integrates the scattered risk levels into critical state determinations, and re-evaluates the patient's current health status to generate critical state analysis results.

[0092] In the fuzzy logic analysis submodule, fuzzy logic analysis is performed using the Scikit-Fuzzy library to process the results of physiological dynamic diagnosis. The data format is numeric, representing the time series of physiological parameters. This submodule defines the membership function of the input variables, selecting the Gaussian distribution as the form of the membership function because the Gaussian distribution can well simulate the natural distribution characteristics of medical data. Subsequently, a fuzzy logic rule base is constructed, which contains the logical relationship between various physiological states and corresponding output states. The input data is processed by the fuzzy inference engine, and the clear physiological parameters are converted into fuzzy values. Finally, the fuzzy logic analysis results are generated. These results describe the fuzzy classification and membership of the physiological parameters in detail, providing a basis for further risk assessment.

[0093] In the risk assessment submodule, risk assessment is performed based on the results of fuzzy logic analysis, and a decision tree model is constructed using the DecisionTreeClassifier from the Scikit-Learn library. The input data is the fuzzy values ​​obtained from the fuzzy logic analysis, including the fuzzy classification and membership information of each physiological parameter. The decision tree is constructed based on the fuzzy values, with a maximum depth of five layers to avoid overfitting. Information gain is used as the node splitting criterion to optimize the decision tree structure. This process converts the fuzzy values ​​into specific risk levels, generating individual patient risk assessment results. These results quantitatively assess the risk for each physiological state, providing clear health risk guidance to physicians and patients.

[0094] In the critical state determination submodule, an expert rule engine is constructed using the PyKnow library based on the individual patient risk assessment results. This submodule integrates the dispersed risk levels and uses the expert system's rule base to determine critical states. The input data is the individual patient risk assessment results generated by the previous submodule, which include risk levels for various physiological states. Based on the pre-defined logical rules and comprehensive consideration of different risk levels, the expert rule engine makes a critical state determination and reassesses the patient's current health status. The resulting critical state analysis provides a comprehensive assessment of the patient's health status, clearly identifying critical health states that require special attention, and providing an important basis for clinical decision-making.

[0095] Consider a scenario involving a test result evaluation system where a patient's blood test data requires health risk assessment. This data set includes white blood cell count, hemoglobin level, and blood glucose level. The simulated values ​​are as follows: white blood cell count of 6500 per cubic millimeter, hemoglobin of 12 grams per deciliter, and blood glucose level of 5.5 millimolar per deciliter. Within the critical state interpretation module, the fuzzy logic analysis submodule first uses the Scikit-Fuzzy Python library to define the membership function of the input variables as a Gaussian distribution based on these physiological dynamic diagnostic results. This module then constructs a rule base, converting the clear physiological parameters into fuzzy values ​​and generating fuzzy logic analysis results. For example, the fuzzy value for blood glucose level can be represented as "normal," "high," or "high." The risk assessment submodule then uses the fuzzy logic analysis results to perform risk assessment using a decision tree algorithm, constructing a decision tree model to convert the fuzzy values ​​into specific risk levels. For example, if the blood glucose level is "high," the patient's health risk level is assigned a "medium" risk level. The criticality assessment submodule then builds an expert rule engine based on the individual patient risk assessment results using the PyKnow library. This engine integrates various risk levels and generates a criticality analysis result. For example, by comprehensively considering the risk levels of white blood cell count, hemoglobin, and blood sugar levels, the patient's current health status may be assessed as "needing further examination."

[0096] See also Figure 2 and Figure 5 ,The process efficiency optimization module includes the bottleneck identification ,submodule, delay prediction submodule, and resource allocation submodule;

[0097] The bottleneck identification submodule uses the Random Forest algorithm based on the critical state analysis results. It uses the RandomForestClassifier of the Scikit-Learn library in the Python environment. The configuration parameters include n_estimators set to 100 and max_depth set to None. It analyzes the data in the inspection process, identifies potential bottlenecks, and locates the links in the process with abnormal efficiency to generate bottleneck identification analysis results.

[0098] The delay prediction submodule performs delay prediction based on the bottleneck identification analysis results. It uses a neural network algorithm, TensorFlow, and Keras libraries to build a multi-stage feature that includes an input layer matching verification process. The hidden layer uses the ReLU activation function and the output layer uses the sigmoid function. The Adam optimizer is set to optimize the model parameters, and the binary_crossentropy loss function is used. The delay that occurs at each stage based on the bottleneck analysis is predicted and the delay prediction analysis results are generated.

[0099] The resource allocation submodule performs resource allocation based on the delay prediction analysis results. It adopts the linear programming method and defines the optimization problem objective as minimizing the total delay time through the PuLP library. The constraints include that resource allocation should not exceed the predetermined budget and resource type restrictions. It also adjusts the resource allocation strategy and process settings and generates process optimization suggestions.

[0100] In the bottleneck identification submodule, the random forest algorithm is used to analyze the data in the inspection process to identify potential bottlenecks and inefficiencies. Specifically, the RandomForestClassifier of the Scikit-Learn library in the Python environment is used, and the configuration parameter n_estimators is 100, which means that 100 decision trees are built. The max_depth is set to None, allowing the tree to grow to the maximum depth to obtain the best split. The data format is mainly structured data, including the time of each stage of the inspection process, equipment usage, and personnel allocation. By analyzing this data, the random forest algorithm can identify the key factors that cause process delays, such as excessive use of a certain equipment or uneven staffing. Finally, the generated bottleneck identification analysis results are presented in the form of a report, which lists in detail the bottlenecks and inefficient links in the inspection process, providing a basis for process optimization.

[0101] In the delay prediction submodule, a neural network algorithm is used to predict delays in the inspection process based on the results of the bottleneck identification analysis. The neural network model, built using the TensorFlow and Keras libraries, consists of an input layer, multiple hidden layers, and an output layer. The input layer matches the multi-stage characteristics of the inspection process, such as the average processing time and waiting time of each stage of the process. The hidden layer uses the ReLU activation function to enhance the model's nonlinear fitting capabilities, and the output layer uses the sigmoid function to predict the probability of delays. The model's optimizer is Adam, and the loss function uses binary_crossentropy, which aims to minimize prediction errors. By training this model, it is possible to predict the delays that will occur in each stage based on the current process configuration. The generated delay prediction analysis results help managers understand which process stages are most likely to become efficiency bottlenecks.

[0102] In the resource allocation submodule, resource allocation is performed to optimize the inspection process based on the delay prediction analysis results. A linear programming approach is used to define the optimization problem through the PuLP library, with the goal of minimizing the total delay time. The constraints take into account that resource allocation should not exceed the predetermined budget and resource type restrictions. By analyzing the output of the delay prediction analysis, such as which process stages have a high probability of delay, and combining it with the actual resource usage, the resource allocation strategy and process settings are adjusted, such as increasing human resources in certain key stages or adjusting equipment usage plans. The final process optimization recommendation report lists in detail the specific measures to improve the efficiency of the inspection process and reduce delays, providing implementation suggestions for achieving process optimization and improving service efficiency.

[0103] Consider a scenario where a large hospital laboratory is optimizing the efficiency of its daily blood testing process. The laboratory processes thousands of blood samples daily across multiple testing procedures. Specific data items in this scenario include sample processing time (average processing time per sample: 15 minutes), equipment utilization (average daily utilization of certain key equipment reaches 90%), and staffing (10 technicians per shift). A random forest algorithm analyzes this data, identifying high equipment utilization as the primary bottleneck, particularly delays caused by high utilization of core analytical equipment. Next, a neural network model is used to predict delays. Simulations indicate that during peak periods, equipment utilization can reach 95%, resulting in at least 20% of sample processing delays exceeding 30 minutes. Based on this prediction, the resource allocation submodule takes action, recommending the addition of a new piece of equipment of the same type and adjusting technician shifts to distribute the peak sample volume. Ultimately, after implementing these optimization recommendations, simulation results show that the utilization of the new equipment drops below 80%, reducing the average sample processing delay to less than 10 minutes, significantly improving the efficiency and responsiveness of the testing process. The detailed data analysis and optimization recommendation reports of this series of operations provide a scientific decision-making basis for laboratory management and effectively improve the quality and efficiency of inspection services.

[0104] See also Figure 2 and Figure 6 ,The health trend prediction module includes a time change analysis submodule, an ,outlier identification submodule, and a trend prediction submodule;

[0105] The time change analysis submodule uses Python's pandas and statsmodels libraries to process time series data based on process optimization recommendations. It constructs an autoregressive integrated moving average model, defines the model's autoregressive term, difference order, and moving average term by setting the order parameter to a specified (p, d, q) value, fits the model using the fit method, and plots the predicted trend of the time series using the plot_predict method, generating a time series trend graph.

[0106] The outlier identification submodule uses the Python scikit-learn library to implement the K-means algorithm based on the time series trend graph. The number of clusters is defined by setting the n_clusters parameter, and the fit_predict method is used to perform cluster analysis on the data. The distance metric is used to identify the distance between each data point and its nearest cluster center. Data points with a distance greater than a specified threshold are identified as outliers, generating abnormal pattern recognition results.

[0107] The trend prediction submodule performs trend prediction based on the results of abnormal pattern recognition. It uses Python's SciPy library for exponential smoothing, sets seasonal adjustment parameters and smoothing parameters through the ExponentialSmoothing class, fits the data using the fit method, and applies the forecast method to predict future values. Adjustments are made based on the impact of outliers to generate trend prediction results.

[0108] In the temporal change analysis submodule, the Autoregressive Integrated Moving Average (ARIMA) model is used to process time series data, aiming to capture trends and patterns in health indicators over time. The data is formatted as a time-stamped sequence, such as blood pressure measurements or blood glucose levels over consecutive weeks. Data preprocessing, such as filling missing values ​​and converting timestamps, is performed using the Python pandas library. The ARIMA model is then constructed using the statsmodels library. The model's order parameters (p, d, q) are set based on the data's autocorrelogram (ACF) and partial autocorrelogram (PACF) to determine the optimal parameter combination. The model is then fitted using the fit method, and the predicted trend of the time series is plotted using the plot_predict method. The resulting time series trend chart reveals the evolution of health indicators over time, providing a visual basis for identifying long-term health changes.

[0109] In the outlier identification submodule, a K-means clustering algorithm is used to identify abnormal patterns based on the time series trend graph. The data format is also a time series, but the focus here is on identifying outliers that deviate from the main trend. The K-means algorithm is implemented using the Python scikit-learn library. The n_clusters parameter determines the number of clusters, selected based on the data distribution and the expected types of abnormal patterns. The fit_predict method is used to perform cluster analysis on the time series data. The distance from each data point to its nearest cluster center is calculated, and data points with a distance greater than a specified threshold are marked as outliers. The abnormal pattern identification results generated by this process reveal potential health risks or measurement errors, providing important information for further analysis.

[0110] In the trend prediction submodule, exponential smoothing is used to predict trends based on the results of outlier pattern recognition. This step uses the ExponentialSmoothing class from the Python SciPy library to optimize the model by setting seasonal adjustment parameters and smoothing parameters. The model fits the data using the fit method, and then uses the forecast method to predict future values. This process specifically considers the impact of outliers and adjusts the forecast strategy accordingly. The resulting trend prediction results are displayed in a chart, showing the future development trends of health indicators after accounting for the impact of outliers. This provides patients with a forecast of their future health status, helping doctors and patients make more informed health management and treatment decisions.

[0111] Consider a scenario where a hospital is managing diabetes patients. Doctors need to assess the patient's blood sugar control trends and identify health risks. The specific data item includes the average monthly blood sugar values ​​for a diabetic patient over the past six months. The simulated values ​​are as follows: 7.8 mmol / L in the first month, 7.5 mmol / L in the second month, 7.2 mmol / L in the third month, 8.0 mmol / L in the fourth month, 8.5 mmol / L in the fifth month, and 9.0 mmol / L in the sixth month. In the Time Change Analysis submodule, these blood sugar values ​​are analyzed using an ARIMA model with model parameters set to (2, 1, 2), representing the configuration of the autoregressive term, differencing order, and moving average term. After fitting the model, the time series forecast trend chart shows a gradual upward trend in blood sugar values. The Outlier Identification submodule analyzes the data using the K-means algorithm with a cluster size of 2. The data is then analyzed and the blood sugar value of 9.0 mmol / L in the sixth month is found to be significantly higher than in other months. It is marked as an outlier, indicating a problem with the patient's blood sugar control. The trend prediction submodule uses the exponential smoothing method to predict the blood glucose level for the next three months, taking into account the existence of outliers. The prediction results show that if no measures are taken, the patient's blood glucose level will continue to rise and reach above 9.5 mmol / L.

[0112] See also Figure 2 and Figure 7 ,The inspection accuracy improvement module includes the variability analysis submodule, the ,influencing factor identification submodule, and the improvement measure proposal submodule;

[0113] The variability analysis submodule performs partial least squares regression analysis based on the trend prediction results. It uses the PLSRegression class in the Python scikit-learn library and sets the number of components parameter n_components to 5 to perform a fitting analysis on the data. By calculating the covariance between each variable and the response variable, it identifies the factors that contribute most to the variability of the test results. It then predicts and analyzes the data set to generate variability source analysis results.

[0114] The influencing factor identification submodule is based on the results of the variability source analysis and applies generalized additive model analysis. It uses the Python pyGAM library and operates through the LinearGAM or GAM class. The smoothing parameter splines is set to 20 and the n_splines parameter is set to automatic selection. The module analyzes the nonlinear relationship between the variability of the test results and potential influencing factors, including selecting the response variable and explanatory variables, and selecting the importance of the explanatory variables through statistical methods to generate the influencing factor identification results.

[0115] Based on the results of variability source analysis and influencing factor identification, the improvement measures proposal sub-module reviews the inspection process and data processing methods, identifies improvement directions, including adjusting the experimental design, optimizing the data processing algorithm, formulating an improvement plan, including adjusting the standardized experimental operation process, and using data analysis technology to improve the consistency of results, and generates improvement measures suggestions.

[0116] In the variability analysis submodule, partial least squares regression analysis is used to further analyze the variability of test results. The data format used is multidimensional numerical data, where each row represents a test result and the columns include various biochemical indicators. Using the PLSRegression class from the Python scikit-learn library, with the number of components parameter n_components set to 5, the goal is to extract the components that have the greatest impact on the response variable from multiple related predictor variables. By calculating the covariance between each variable and the response variable, the factors that contribute most to the variability of the test results are identified. The variability source analysis results generated by this process not only reveal the main factors affecting test accuracy but also provide a scientific basis for further optimizing the testing process.

[0117] In the influencing factor identification submodule, based on the results of the variability source analysis, a generalized additive model (GAM) was applied to conduct a more refined influencing factor analysis. This process was implemented using the Python pyGAM library, using the LinearGAM or GAM classes. The smoothing parameter splines was set to 20, and the n_splines parameter was automatically selected to accommodate the nonlinear characteristics of the data and reveal the complex relationship between the variability of the test results and potential influencing factors. This approach enabled the model to select and quantify explanatory variables that significantly influenced the variability of the test results. The resulting influencing factor identification results provided accurate guidance for developing targeted improvement measures.

[0118] In the improvement proposal submodule, based on the variability source analysis results and the influence factor identification results, a comprehensive review of the test process and data processing methods is conducted. By considering the key influencing factors identified in the test process, a series of improvement measures are developed, including adjusting the experimental design, optimizing the data processing algorithm, and adjusting the standardized process of experimental operation. In particular, data analysis techniques are used to improve the consistency of the results, such as introducing more advanced data preprocessing and analysis methods to reduce the impact of external interference and operational errors on test results. The generated improvement measure proposal report details the implementation recommendations for each measure, aiming to significantly improve the accuracy and reliability of medical testing through scientific data analysis and process optimization.

[0119] Assume that in a medical research center, researchers are conducting a long-term tracking study on a group of patients' tumor marker levels, aiming to identify factors affecting tumor marker variability and propose measures to improve diagnostic accuracy. The specific data items include patients' age, gender, tumor type, treatment history, and monthly tumor marker measurements over the past year. The simulated numerical example is a 45-year-old male patient with colorectal cancer who received chemotherapy treatment, with tumor marker levels of 150, 145, 147, 155, 160, 158, 162, 170, 175, 180, 185, 190 units / ml. In the variability analysis submodule, partial least squares regression is used to analyze these data, setting the number of components parameter n_components to 5, to identify significant correlations between chemotherapy history and tumor marker levels, generating variability source analysis results, revealing the main contribution of treatment history to tumor marker variability. The influence factor identification submodule applies generalized additive models analysis, setting the smoothing parameter splines to 20, to analyze the non-linear relationship between tumor marker levels and age, gender, and identify age as an important factor affecting tumor marker levels, generating influence factor identification results. The improvement proposal submodule is based on the above analysis results, proposing to adjust the test process, suggesting adjusting the interpretation standards of tumor markers based on considering patients' treatment history and age information, to improve the accuracy of diagnosis, generating specific improvement measure proposal documents.

[0120] Please refer to Figure 2 and Figure 8 , the result interpretation optimization module includes feature analysis optimization submodule, knowledge integration submodule, interpretation accuracy improvement submodule;

[0121] The feature analysis optimization submodule optimizes the feature analysis of the conditional random field model based on the improvement measures. Through the sklearn_crfsuite library of Python, the regularization degree of the model is adjusted by setting the parameters of the CRF class as algorithm = lbfgs, c1 = 0.1, and c2 = 0.1. The model is trained through the training data set. The fit method is used to analyze the features and their interactions in the medical test results, extract the key information related to the result interpretation, and generate the feature analysis results.

[0122] The knowledge integration submodule integrates prior knowledge based on the feature analysis results. By setting a custom function, the conditional random field model is added with prior knowledge in the medical field. The model's recognition ability is optimized using information, and the knowledge integration results are generated.

[0123] The interpretation accuracy optimization submodule optimizes the interpretation accuracy based on the knowledge integration results. By optimizing the parameters of the conditional random field model, the model is fully learned by setting max_iterations = 1000. The predict method is applied to interpret the new test results, and the refined interpretation results are generated.

[0124] In the feature analysis optimization submodule, the conditional random field (CRF) model is used to optimize the feature analysis to improve the interpretation accuracy of medical test results. The data format covers various parameters of medical tests, such as biochemical indicators and clinical symbols. These data are integrated into a training data set. Using the sklearn_crfsuite library of Python, the parameter algorithm of the CRF model is set to lbfgs, and the regularization parameters c1 and c2 are set to 0.1. Such settings help prevent model overfitting while maintaining sufficient flexibility. Through the fit method, the model learns the interactions between features and results in the training data set, thereby optimizing the feature analysis process. The feature analysis results generated by this process reveal which features in the test results are most critical for diagnosis, providing an accurate basis for subsequent interpretation.

[0125] The knowledge integration submodule further integrates prior knowledge in the medical field to optimize the conditional random field model based on the feature analysis results. Through a custom function, the CRF model is added with medical field knowledge, such as disease diagnosis guidelines and typical thresholds of biomarkers. The integration of these prior knowledge not only improves the model's recognition ability for specific medical conditions, but also enhances the model's accuracy in interpreting medical test results. The generated knowledge integration results file contains the new recognition ability evaluation of the model after applying these prior knowledge, showing that the model's understanding and interpretation ability for complex medical test data have been significantly improved.

[0126] Based on this knowledge integration, the Interpretation Accuracy Optimization submodule further optimizes the parameters of the conditional random field model to maximize interpretation accuracy. Setting the max_iterations parameter to 1000 ensures sufficient model iteration during the learning process, capturing more subtle data patterns. The predict method is applied to interpret new medical test results. This step generates refined interpretations, detailing the diagnosis and associated medical information for each test result. These refined interpretations provide physicians with more precise diagnostic evidence, helping to improve the quality and efficiency of clinical decision-making.

[0127] Suppose that in a general hospital, the medical team is using a test result evaluation system to optimize the diagnostic process for patients with chronic liver disease. Specific data items include the patient's age, gender, liver function test results (such as ALT, AST, ALP, and bilirubin levels), and the results of liver ultrasound examinations. The simulated numerical example is a 56-year-old male patient with an ALT of 45U / L, an AST of 50U / L, an ALP of 85U / L, and a total bilirubin of 1.2mg / dL. Ultrasound examination showed mild fatty degeneration of the liver. In the feature analysis and optimization submodule, the characteristics of these test results and their interactions were analyzed through the conditional random field model. Identifying the interaction between ALT and AST is of key significance for the diagnosis of hepatitis status. The knowledge integration submodule further incorporates prior knowledge from the liver disease diagnosis guidelines into the model, such as the association rules between ALT and AST levels and hepatitis activity, to optimize the model's interpretation ability. Building on this foundation, the Interpretation Accuracy Optimization submodule conducted deep learning and applied 1,000 iterations of the optimization process to ultimately refine the patient's test results, predicting a moderate risk of non-alcoholic fatty liver disease (NAFLD) and recommending a further liver biopsy. This refined interpretation provided doctors with a more accurate diagnosis, enabling targeted treatment recommendations for patients, improving treatment outcomes and patient satisfaction.

[0128] See also Figure 2 and Figure 9 ,The potential pathology mining module includes cluster identification submodule, health status classification submodule, and pathology pattern analysis submodule;

[0129] The cluster identification submodule performs cluster analysis based on the refined interpretation results. It uses the K-means algorithm and the KMeans class implemented by the Python scikit-learn library. The n_clusters parameter is set to estimate the number of clusters based on the data characteristics. The fit_predict method is used to cluster the medical test result data, identify patient groups with similar patterns, and generate cluster grouping results.

[0130] The health status classification submodule is based on the clustering grouping results. It uses the DecisionTreeClassifier class of the scikit-learn library to set the max_depth parameter to control the maximum depth of the tree to prevent overfitting, and then classifies the clustering results into health status to generate health status classification results.

[0131] The pathology pattern analysis submodule performs multivariate statistical analysis based on the health status classification results, including principal component analysis and linear discriminant analysis. This is performed using the scikit-learn library. The n_components parameter is set to select the number of principal components, revealing pathology patterns among multiple health status classifications and generating potential pathology analysis results.

[0132] In the Cluster Identification submodule, cluster analysis is performed using the K-means algorithm to identify groups of patients with similar patterns of medical test results. The data format used consists of multidimensional patient test results, such as blood, urine, and imaging data, converted into numerical datasets for the algorithm to process. The KMeans class is implemented using the Python scikit-learn library, with the n_clusters parameter set to an estimated number of clusters based on data characteristics. The data is processed using the fit_predict method. The resulting cluster groupings help identify groups of patients with similar health conditions or pathologies, providing a foundation for targeted treatment and further research.

[0133] In the health status classification submodule, a decision tree algorithm is used to classify patients' health status based on the clustering results. The data format is the same as the group labels after cluster analysis, combined with the patient's specific health information, such as diagnosis and treatment response. The decision tree model is implemented using the DecisionTreeClassifier class in the scikit-learn library. The max_depth parameter is adjusted to prevent overfitting and ensure model generalization. This step generates health status classification results that more finely categorize patients into different health status levels or pathology types, providing doctors with more accurate diagnostic references.

[0134] The pathology pattern analysis submodule uses multivariate statistical analysis methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), to conduct in-depth analysis based on the health status classification results. The data format processed by these methods is a high-dimensional patient feature set, including the classification information obtained from the first two submodules. Implemented through the scikit-learn library, the n_components parameter helps determine the number of principal components considered in the analysis. This process reveals pathology patterns and associations between different health status classifications. The generated potential pathology analysis results provide new insights for medical research and clinical practice, especially in understanding the mechanisms of complex diseases and discovering new therapeutic targets.

[0135] Suppose a research team at a large medical center is conducting a study on a population of cardiovascular disease patients. The goal is to identify underlying pathological patterns and risk factors for cardiovascular disease through medical test results. Specific data items include the patient's age, gender, blood pressure readings, cholesterol levels, blood glucose levels, and electrocardiogram (ECG) results. The simulated data example includes a sample of patient data, such as a 56-year-old male patient with a blood pressure of 145 / 90 mmHg, a total cholesterol level of 220 mg / dL, a blood glucose level of 5.6 mmol / L, and an ECG showing mild ST-segment elevation. In the cluster identification submodule, this data is processed using the K-means algorithm. Assuming the number of clusters is estimated to be three based on the data characteristics, cluster analysis reveals three patient groups with similar patterns of medical test results, reflecting varying degrees of cardiovascular disease risk. In the health status classification submodule, based on the clustering results, a decision tree algorithm is used to further classify these groups into three health states: "low risk," "medium risk," and "high risk." Setting max_depth to 4 prevents overfitting while ensuring classification accuracy. The pathology pattern analysis submodule uses principal component analysis and linear discriminant analysis to analyze the main components and pathology patterns of different health status groups, revealing that hypertension and high cholesterol are the main risk factors for cardiovascular disease in high-risk groups. This potential pathology analysis provides new insights into the prevention and treatment of cardiovascular disease, especially in the design of interventions for high-risk patient groups.

[0136] See also Figure 2 and Figure 10 ,The comprehensive health assessment module includes a comprehensive risk assessment submodule, a ,trend comprehensive analysis submodule, and a pathology comprehensive judgment submodule;

[0137] The comprehensive risk assessment submodule uses a comprehensive assessment algorithm to evaluate the patient's health status based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measures suggestions, detailed interpretation results, and potential pathology analysis results. It combines multi-source data through a comprehensive weighted scoring model. Parameters include the influence weight of each data source to classify the patient's health risk and generate a comprehensive risk rating result.

[0138] The trend comprehensive analysis submodule performs time series analysis and trend forecasting based on the comprehensive risk rating results. It uses the autoregressive integral moving average model to predict long-term health trends. The parameter settings reflect the changing patterns of historical data, including the difference level and the number of autoregressive terms, to predict future changes in health status and generate health trend forecast analysis results.

[0139] The pathology comprehensive judgment submodule performs a comprehensive pathology judgment based on the health trend prediction analysis results and the potential pathology analysis results. It uses logistic regression analysis to evaluate the development probability of a specified pathological state. The parameters include regression coefficients, which are adjusted based on the patient's health data and potential pathology characteristics to provide advice for medical decision-making and generate comprehensive health assessment results.

[0140] The comprehensive risk assessment submodule utilizes a comprehensive assessment algorithm to analyze data from various submodules, including physiological dynamic diagnosis, criticality analysis, process optimization recommendations, trend predictions, improvement measures, detailed interpretation results, and potential pathology analysis results. These data sources are integrated and processed through a weighted scoring model, where the influence of each data source is carefully weighted to ensure the accuracy and reliability of the assessment results. This process automatically categorizes patients' health risks using an algorithm, generating a comprehensive risk rating report that provides a critical basis for medical decision-making.

[0141] The comprehensive trend analysis submodule utilizes time series analysis and the ARIMA model for trend forecasting to predict long-term trends in health data. By precisely configuring model parameters, such as the differencing level and the number of autoregressive terms, this module captures the dynamic trends of health status over time and generates a detailed health trend forecast report. This report not only depicts future changes in a patient's health status but also provides a scientific basis for preventive health interventions.

[0142] The Comprehensive Pathology Assessment submodule uses logistic regression analysis to assess the probability of specific pathological conditions developing based on health trend prediction analysis results and potential pathology analysis results. This process considers the patient's health data and potential pathological characteristics, precisely adjusting the parameters of the regression model and ultimately generating a detailed Comprehensive Pathology Assessment report. This report provides physicians with in-depth insights into the development of a patient's pathological condition, helping them make more precise medical decisions.

[0143] Assume that in a study on cardiovascular disease risk assessment, a test result evaluation system is used to analyze and predict a patient's health risk. The data items include but are not limited to the patient's blood pressure readings, cholesterol levels, glycosylated hemoglobin percentage, body mass index (BMI), and historical cardiovascular event records. The specific simulated values ​​are as follows: blood pressure reading 120 / 80 mmHg, cholesterol level 200 mg / dL, glycosylated hemoglobin percentage 5.6%, body mass index 25 kg / m 2 , no history of cardiovascular events. These data were comprehensively considered using a comprehensive assessment algorithm, with the weighting of each data item adjusted to ensure the accuracy of the assessment results. For example, the weighting of cholesterol level was 0.3, blood pressure readings 0.25, body mass index 0.2, glycated hemoglobin ratio 0.15, and a history of cardiovascular events 0.1. After processing these data using a weighted scoring model, the patient was assigned to the low-risk group, resulting in a comprehensive risk rating of "low risk" with a score of 78 out of 100. The trend comprehensive analysis submodule used an ARIMA model with parameters set to (2, 1, 2), reflecting the number of autoregressive terms, differencing levels, and moving average terms in the patient's health data. Based on these parameters, the model predicted that the patient's health status would remain stable over the next 12 months, with no significant deterioration. The pathology comprehensive judgment submodule, using logistic regression analysis, estimated the patient's probability of developing cardiovascular disease in the future to be 15%, indicating that given their current health status and lifestyle, their risk of cardiovascular disease was low.

[0144] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0145] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A test result evaluation system, characterized in that: The system comprises: The physiological dynamic recognition module simulates time series data, analyzes parameter changes, and generates physiological dynamic diagnosis results; The physiological dynamic identification module includes a steady-state analysis submodule, a periodicity identification submodule, and a chaotic state judgment submodule; The steady-state analysis submodule is based on time series data from medical tests and is constructed using a nonlinear dynamic model. It uses the integrate module in Python's SciPy library for numerical integration, with parameters including time step and initial conditions, to simulate the dynamic properties of physiological activities. Through attractor and bifurcation theory analysis, the Matplotlib library is used to graphically display parameter changes, identify whether physiological activities have reached a stable state, and generate stable state characteristic analysis results. The periodicity identification submodule, based on the results of the steady-state feature analysis, again applies Fourier transform to detect periodic fluctuations. The transformation is performed using the NumPy library's FFT function, with parameters such as sampling rate and data volume set to find repetitive patterns or periodic fluctuations in the time series. The periodic properties of physiological activities are selected through spectral analysis to generate periodic fluctuation detection results. The chaotic state judgment submodule uses the Lyapunov exponent to evaluate the chaotic state based on the periodic fluctuation detection results. The Lyapunov exponent is calculated using the Python Nolds library. The parameters include the embedding dimension and the data sequence. The module evaluates the sensitivity of the physiological process dynamics to the initial conditions and its unpredictability. The calculation results of the Lyapunov exponent are used to determine whether the physiological activity exhibits chaotic characteristics and generate physiological dynamic diagnosis results. The critical state interpretation module analyzes the boundary value of the test result based on the physiological dynamic diagnosis result and generates a critical state analysis result; The critical state interpretation module includes a fuzzy logic analysis submodule, a risk assessment submodule, and a critical state determination submodule; The fuzzy logic analysis submodule performs fuzzy logic analysis based on the physiological dynamic diagnosis results. It defines the membership function of the input variables through the Scikit-Fuzzy library in Python. The membership function is set to Gaussian distribution according to the distribution of medical data, and a rule base is constructed. The input data is processed through the fuzzy inference engine, and the clear physiological parameters are converted into fuzzy values ​​to generate fuzzy logic analysis results. The risk assessment submodule uses a decision tree algorithm based on the results of fuzzy logic analysis and is executed through the DecisionTreeClassifier function in the Scikit-Learn library. The decision tree is constructed based on the fuzzy values, with a maximum depth of 5 layers to avoid overfitting. Information gain is used as the node splitting criterion to convert the fuzzy values ​​into risk levels. Based on the fuzzy classification provided by fuzzy logic, the risk under each physiological state is quantitatively assessed to generate a risk assessment result for the individual patient. The critical state determination submodule uses the PyKnow library to build an expert rule engine based on the risk assessment results of individual patients, integrates the scattered risk levels into critical state determinations, and re-evaluates the patient's current health status to generate critical state analysis results; The process efficiency optimization module analyzes and verifies process data based on the critical state analysis results, adjusts resource allocation, and generates process optimization suggestions; The health trend prediction module analyzes the trend of changes in test results based on the process optimization suggestions, predicts changes in health status, and generates trend prediction results; The inspection accuracy improvement module analyzes the variability of the inspection results based on the trend prediction results and generates improvement measures. The result interpretation optimization module interprets the medical test results based on the improvement measures suggested, integrates feature extraction and prior knowledge, and generates detailed interpretation results; The potential pathology mining module analyzes the medical test results based on the refined interpretation results to generate potential pathology analysis results; The comprehensive health assessment module evaluates and comprehensively judges the health status based on all the above results and generates comprehensive health assessment results; The physiological dynamic diagnosis results include stability parameters, periodic fluctuation frequency, and chaotic dynamic characteristic values; the critical state analysis results include fuzzy logic judgment level, boundary value sensitivity index, and state transition warning signal; the process optimization suggestions include key equipment scheduling priority, detection step simplification plan, and emergency response mechanism adjustment; the trend prediction results include health status evolution trend chart, abnormal health indicator list, and risk disease warning mark; the improvement measures suggestions include inspection accuracy optimization, data processing algorithm update, and experimental operation standardization process; the detailed interpretation results include key biomarker analysis, disease correlation score, and treatment response potential indicator; the potential pathology analysis results include unmanifested pathological state signals, group health trend deviation, and predictive disease classification map; the comprehensive health assessment results include individual health comprehensive score, potential health risk level and health management suggestions.

2. The test result evaluation system according to claim 1, characterized in that: The process efficiency optimization module includes a bottleneck identification submodule, a delay prediction submodule, and a resource allocation submodule; The bottleneck identification submodule uses the Random Forest algorithm based on the critical state analysis results. It uses the RandomForestClassifier of the Scikit-Learn library in the Python environment, with configuration parameters including n_estimators set to 100 and max_depth set to None. It analyzes the data in the inspection process, identifies potential bottlenecks, and locates the links with abnormal efficiency in the process to generate bottleneck identification analysis results. The delay prediction submodule performs delay prediction based on the bottleneck identification analysis results. It uses a neural network algorithm, through the TensorFlow and Keras libraries, to build a multi-stage feature including an input layer matching verification process, uses the ReLU activation function in the hidden layer, and the sigmoid function in the output layer. The optimizer is set to Adam to optimize the model parameters, and the loss function uses binary_crossentropy. It predicts the delay that occurs at each stage based on the bottleneck analysis and generates a delay prediction analysis result. The resource allocation submodule performs resource allocation based on the delay prediction analysis results. It adopts linear programming method and defines the optimization problem objective as minimizing the total delay time through the PuLP library. The constraints include that resource allocation should not exceed the predetermined budget and resource type restrictions. It also adjusts the resource allocation strategy and process settings and generates process optimization suggestions.

3. The test result evaluation system according to claim 1, characterized in that: The health trend prediction module includes a time change analysis submodule, an outlier identification submodule, and a trend prediction submodule; The time change analysis submodule uses Python's pandas and statsmodels libraries to process time series data based on process optimization suggestions, constructs an autoregressive integrated moving average model, defines the model's autoregressive term, difference order, and moving average term by setting the order parameter to a specified (p, d, q) value, fits the model using the fit method, plots the predicted trend of the time series using the plot_predict method, and generates a time series change trend graph; The outlier identification submodule uses the Python scikit-learn library to implement the K-means algorithm based on the time series change trend graph, defines the number of clusters by setting the n_clusters parameter, uses the fit_predict method to perform cluster analysis on the data, uses a distance metric to identify the distance between each data point and its nearest cluster center, identifies data points with a distance greater than a specified threshold as outliers, and generates an abnormal pattern recognition result; The trend prediction submodule performs trend prediction based on the abnormal pattern recognition results, uses Python's SciPy library to perform exponential smoothing, sets seasonal adjustment parameters and smoothing parameters through the ExponentialSmoothing class, fits the data using the fit method, applies the forecast method to predict future values, makes adjustments based on the impact of abnormal values, and generates trend prediction results.

4. The test result evaluation system according to claim 1, wherein: The test accuracy improvement module includes a variability analysis submodule, an influencing factor identification submodule, and an improvement measure proposal submodule; The variability analysis submodule performs partial least squares regression analysis based on the trend prediction results. It uses the PLSRegression class in the Python scikit-learn library and sets the number of components parameter n_components to 5 to perform fitting analysis on the data. By calculating the covariance between each variable and the response variable, it identifies the factors that contribute most to the variability of the test results, predicts and analyzes the data set, and generates variability source analysis results. The influencing factor identification submodule is based on the results of the variability source analysis and applies generalized additive model analysis. It uses the Python pyGAM library and operates through the LinearGAM or GAM class. The smoothing parameter splines is set to 20 and the n_splines parameter is set to automatic selection. The module analyzes the nonlinear relationship between the variability of the test results and the potential influencing factors, including selecting the response variable and the explanatory variables, and selecting the importance of the explanatory variables through statistical methods to generate the influencing factor identification results. The improvement measure proposal submodule reviews the inspection process and data processing methods based on the results of variability source analysis and influencing factor identification, identifies improvement directions, including adjusting the experimental design, optimizing the data processing algorithm, formulating an improvement plan, including adjusting the experimental operation standardization process, and using data analysis technology to improve the consistency of results, and generates improvement measure suggestions.

5. The test result evaluation system according to claim 1, characterized in that: The result interpretation optimization module includes a feature analysis optimization submodule, a knowledge integration submodule, and an interpretation accuracy improvement submodule; The feature analysis and optimization submodule performs feature analysis and optimization of the conditional random field model based on the improvement measures suggested. Using the Python sklearn_crfsuite library, the model regularization degree is adjusted by setting the parameters of the CRF class: algorithm=lbfgs, c1=0.1, and c2=0.

1. The model is trained using a training dataset and the fit method is used to analyze the features and their interactions in the medical test results, extracting key information related to the interpretation of the results, and generating feature analysis results. The knowledge integration submodule integrates prior knowledge based on the feature analysis results, adds prior knowledge in the medical field to the conditional random field model by setting a custom function, and uses the information to optimize the recognition ability of the model to generate knowledge integration results; The interpretation accuracy optimization submodule further optimizes the interpretation accuracy based on the knowledge integration results. By optimizing the parameters of the conditional random field model and setting max_iterations=1000 to fully learn the model, the predict method is applied to interpret the new test results to generate refined interpretation results.

6. The test result evaluation system according to claim 1, characterized in that: The potential pathology mining module includes a cluster identification submodule, a health status classification submodule, and a pathology pattern analysis submodule; The cluster identification submodule performs cluster analysis based on the refined interpretation results. It uses the K-means algorithm and executes the KMeans class through the Python scikit-learn library. It sets the n_clusters parameter to estimate the number of groups based on data characteristics and uses the fit_predict method to cluster the medical test result data, identify patient groups with similar patterns, and generate cluster grouping results. The health status classification submodule is based on the clustering grouping results, and uses the DecisionTreeClassifier class of the scikit-learn library to set the max_depth parameter to control the maximum depth of the tree to prevent overfitting, and performs health status classification on the clustering results to generate health status classification results; The pathology pattern analysis submodule performs multivariate statistical analysis based on the health status classification results, including principal component analysis and linear discriminant analysis, using the scikit-learn library. The n_components parameter is set to select the number of principal components, revealing pathology patterns among multiple health status classifications and generating potential pathology analysis results.

7. The test result evaluation system according to claim 1, characterized in that: The comprehensive health assessment module includes a comprehensive risk assessment submodule, a trend comprehensive analysis submodule, and a pathology comprehensive judgment submodule; The comprehensive risk assessment submodule uses a comprehensive assessment algorithm to assess the patient's health status based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measures suggestions, detailed interpretation results, and potential pathology analysis results. It combines multi-source data through a comprehensive weighted scoring model, with parameters including the influence weight of each data source, to classify the patient's health risk and generate a comprehensive risk rating result; The trend comprehensive analysis submodule performs time series analysis and trend forecasting based on the comprehensive risk rating results, and uses the autoregressive integrated moving average model to predict long-term health trends. The parameter settings reflect the changing patterns of historical data, including the difference level and the number of autoregressive terms, to predict future changes in health status and generate health trend forecast analysis results; The pathology comprehensive judgment submodule performs a comprehensive pathology judgment based on the health trend prediction analysis results and the potential pathology analysis results, and uses logistic regression analysis to evaluate the development probability of a specified pathological state. Parameters include regression coefficients, which are adjusted based on the patient's health data and potential pathology characteristics to provide advice for medical decision-making and generate comprehensive health assessment results.

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