Test result evaluation system

Abstract

The invention discloses an examination result evaluation system in the technical field of medical examination evaluation. The system comprises a physiological dynamic recognition module, a critical state interpretation module, a process efficiency optimization module, a health trend prediction module, an examination accuracy improvement module, a result interpretation optimization module, a potential pathology mining module and a health evaluation comprehensive module. According to the method, the depth and the breadth of medical examination and evaluation are remarkably improved by introducing technologies such as a nonlinear dynamic model, fuzzy logic and a machine learning algorithm. Firstly, the application of the physiological dynamic recognition module enables the analysis of time sequence data to be more accurate, and subtle changes of physiological behaviors can be revealed, so that the capability of early recognition of diseases is improved. The introduction of the fuzzy logic shows higher flexibility and accuracy in the aspects of uncertainty and fuzziness of processing test results, and particularly provides finer support for medical decision making in the aspect of judgment of boundary values and critical states.

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 test evaluation technology focuses on the development and application of various tools, methods and systems to accurately and effectively analyze and interpret medical test results. This field covers a wide range of aspects, from biomarker detection, disease diagnosis, efficacy evaluation to patient health monitoring. Technical means include but are not limited to biochemical analysis, molecular biology techniques, data processing software and artificial intelligence algorithms. These technological advances enable medical test evaluation to not only provide more accurate diagnostic information, but also help doctors make more precise treatment recommendations.

[0003] The test result evaluation system is a system that uses computer technology, artificial intelligence, statistical methods, etc. to analyze and interpret medical test results. Its purpose is to improve the accuracy of test results and the effectiveness of interpretation, reduce human errors, and speed up diagnosis, thereby providing patients with more accurate and timely medical services. Through in-depth analysis of test data, this system can reveal potential health problems and assist doctors in making more informed decisions in complex clinical situations. Ultimately, the system aims to improve patient treatment outcomes and health status by improving the quality and efficiency of medical tests.

[0004] Although existing technologies have achieved remarkable results in the field of medical test evaluation, there is a problem of insufficient analytical accuracy in dealing with the dynamic changes of complex physiological and pathological processes. In particular, in the in-depth analysis of time series data and the simulation of physiological behavior, traditional technologies have difficulty in capturing subtle system dynamic changes, resulting in limited early identification of potential pathological states. In addition, when dealing with the uncertainty and ambiguity in medical test results, the interpretation ability of existing systems is limited, and it is difficult to provide a delicate and accurate health status assessment, especially in the analysis of boundary values ​​and critical states. The lack of flexibility and fine-grained interpretation affects the accuracy of medical decision-making. In terms of test process optimization, traditional technologies lack the ability to monitor and predict the test process in real time, resulting in bottlenecks and delays in the process. The problem is difficult to be discovered and solved in a timely manner, affecting the test efficiency and service response time. For the differential analysis and pattern recognition of test results, traditional technologies have failed to make full use of statistical principles and pattern recognition technology, limiting the ability to identify subtle differences and their change patterns, and weakening the accuracy of predicting potential health problems 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 purpose of the present invention is to provide a test result evaluation system to solve the problem raised in the above background technology that although the existing technology has achieved remarkable results in the field of medical test evaluation, it has insufficient analysis accuracy in dealing with the dynamic changes of complex physiological and pathological processes. Especially in the in-depth analysis of time series data and the simulation of physiological behavior, traditional technology is difficult to capture subtle system dynamic changes, resulting in limited early recognition of potential pathological states. In addition, when dealing with uncertainty and ambiguity in medical test results, the interpretation ability of existing systems is limited, and it is difficult to provide delicate and accurate health status assessment, especially in the analysis of boundary values ​​and critical states, lacking flexibility and fine-grained interpretation, affecting the accuracy of medical decision-making. In terms of test process optimization, traditional technology lacks the ability to monitor and predict the test process in real time, resulting in bottlenecks and delays in the process. Problems are difficult to be discovered and solved in time, affecting test efficiency and service response time. For the difference analysis and pattern recognition of test results, traditional technology fails to make full use of statistical principles and pattern recognition technology, limiting the ability to recognize subtle differences and their change patterns, and weakening the accuracy of predicting potential health problems and disease trends. Finally, in terms of tools for analyzing test result variability, 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 test process and the consistency of the results.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a test result evaluation system, the 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 health evaluation comprehensive module;

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

[0009] The critical state interpretation module uses fuzzy logic method 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 network 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 according to the prediction results, and generate process optimization suggestions;

[0011] The health trend prediction module is based on process optimization suggestions, 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 variability sources and quantification of result fluctuations, and generates suggestions for 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 detailed 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 is 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 uses a comprehensive assessment algorithm 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 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 deviations, and predictive disease classification maps; the comprehensive health assessment results include individual health comprehensive scores, potential health risk levels, and health management suggestions.

[0017] Preferably, the physiological dynamic identification module includes a steady-state analysis submodule, a periodicity identification submodule, and a chaotic state judgment submodule;

[0018] The steady-state analysis submodule is constructed based on the time series data of medical examinations using a nonlinear dynamic model, and uses the integrate module in Python's SciPy library for numerical integration. The parameters include time step and initial conditions, simulate the dynamic properties of physiological activities, analyze the attractor and bifurcation theory, and use the Matplotlib library to graphically display parameter changes, identify whether the physiological activity has reached a stable state, and generate stable state characteristic analysis results;

[0019] The periodicity identification submodule applies Fourier transform to detect periodic fluctuations again based on the stable state feature analysis results, performs the transformation through the fft function of the NumPy library, sets parameters including sampling rate and data volume, finds repetitive patterns or periodic fluctuations in the time series, selects the periodic properties of physiological activities through spectrum analysis, and generates periodic fluctuation detection results;

[0020] The chaotic state judgment submodule is based on the periodic fluctuation detection results, uses the Lyapunov exponent to evaluate the chaotic state, and uses Python's Nolds library to calculate the Lyapunov exponent. The parameters include the embedding dimension and the data sequence, and evaluate the sensitivity of the physiological process dynamics to the initial conditions and its unpredictability. Through the calculation results of the Lyapunov exponent, it is determined whether the physiological activity exhibits chaotic characteristics and generates physiological dynamic diagnosis results.

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

[0022] The fuzzy logic analysis submodule performs fuzzy logic analysis based on the physiological dynamic diagnosis results. The membership function of the input variable is defined through the Scikit-Fuzzy library in Python. The membership function is set as a Gaussian distribution according to the distribution of medical data, and a rule library 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 is based on the fuzzy logic analysis results, adopts the decision tree algorithm, and is executed through the DecisionTreeClassifier function in the Scikit-Learn library. The decision tree is constructed based on the fuzzy value, and the maximum depth is set to 5 layers to avoid overfitting. Information gain is used as the node splitting criterion to convert the fuzzy value into a risk level. According to the fuzzy classification provided by the fuzzy logic, the risk under each physiological state is quantitatively evaluated to generate the risk assessment results of 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 is based on the critical state analysis results, and uses the RandomForestClassifier of the Scikit-Learn library in the Python environment through the random forest algorithm. 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 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, uses a neural network algorithm, and builds a multi-stage feature including an input layer matching inspection process through TensorFlow and Keras libraries. The hidden layer uses the ReLU activation function, the output layer uses the sigmoid function, and the optimizer is set to Adam to optimize the model parameters. The loss function uses binary_crossentropy to predict the delay that occurs at each stage based on the bottleneck analysis and generate a delay prediction analysis result;

[0028] The resource allocation submodule performs resource allocation based on the delay prediction analysis results, 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, and adjusts the resource allocation strategy and process settings to generate process optimization suggestions.

[0029] Preferably, the health trend prediction module includes a time change analysis submodule, an abnormal value 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, builds an autoregressive integrated moving average model, defines the autoregressive term, difference order, and moving average term of the model by setting the order parameter to the 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 is based on the time series change trend graph, uses Python's scikit-learn library to implement the K-means algorithm, defines the number of clusters by setting the n_clusters parameter, uses the fit_predict method to perform cluster analysis on the data, uses the distance metric to identify the distance between each data point and its nearest cluster center, identifies data points whose distance is 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 for 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, uses the PLSRegression class in the scikit-learn library of Python, sets the number of components parameter n_components to 5, performs fitting analysis on the data, calculates the covariance between each variable and the response variable, 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 variability source analysis results, applies generalized additive model analysis, uses Python's pyGAM library, operates through LinearGAM or GAM class, sets the smoothing parameter splines to 20, and the n_splines parameter to automatic selection, analyzes the nonlinear relationship between the variability of the test results and the potential influencing factors, including selecting the response variable and the explanatory variable, selecting the importance of the explanatory variable through statistical methods, and generating 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, and formulating improvement plans, including adjusting the standardized experimental operation 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 optimization submodule performs feature analysis optimization of the conditional random field model based on the improvement measures suggested. Through 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 through the training data set, and the features and their interactions in the medical test results are analyzed using the fit method, and the key information related to the interpretation of the results is extracted to 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, optimizes the recognition ability of the model using information, and generates knowledge integration results;

[0040] The interpretation accuracy optimization submodule further optimizes the interpretation accuracy based on the knowledge integration result, 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, adopts the K-means algorithm, executes the KMeans class through the Python scikit-learn library, sets the n_clusters parameter to estimate the number of groups according to the data characteristics, and uses the fit_predict method to cluster the medical test result data, identifies the patient groups with similar patterns, and generates 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 classifies the clustering results into health status to generate health status classification results;

[0044] The pathological pattern analysis submodule performs multivariate statistical analysis, including principal component analysis and linear discriminant analysis, based on the health status classification results through the scikit-learn library, setting the n_components parameter to select the number of principal components, revealing the pathological patterns among multiple health status classifications, and generating potential pathological 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 and calculates through a comprehensive weighted scoring model. The parameters include the influence weight of each data source, and the patient's health risk is graded to 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 items, 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 the prior art, the beneficial effects of the present invention are as follows: by introducing technologies such as nonlinear dynamic models, fuzzy logic, and machine learning algorithms, the depth and breadth of medical test evaluation are significantly improved. First, the application of the physiological dynamic recognition module makes the analysis of time series data more accurate, and can reveal subtle changes in physiological behavior, thereby improving the ability to identify diseases in the early stage. The introduction of fuzzy logic shows higher flexibility and accuracy in dealing with the uncertainty and ambiguity of test results, especially in the judgment of boundary values ​​and critical states, providing more delicate support for medical decision-making. In addition, through the implementation of the process efficiency optimization module, bottlenecks and delays in the test process can be discovered and solved in a timely manner, significantly improving the test efficiency and response speed. The combination of the health trend prediction module and the potential pathology mining module provides strong data support for identifying the changing trend of the patient's health status and the potential disease risk, and enhances 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 test process and reduces the variability of the results, but also improves the accuracy and efficiency of the test result interpretation, provides an improvement strategy for the laboratory, and fundamentally improves the stability of the test process and the consistency of the results. In summary, the present invention has brought about a comprehensive improvement in the field of medical testing and evaluation, especially showing remarkable effects in improving diagnostic accuracy, optimizing testing procedures, enhancing predictive capabilities and improving health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying 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 is proposed for the present invention;

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

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

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

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

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

[0059] Fig. 9 A schematic diagram of a potential pathology mining module in a test result evaluation system is proposed for the present invention;

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

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are 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 identification module is based on the time series data of medical examinations, uses nonlinear dynamic models and attractor analysis to simulate physiological behaviors, analyzes the impact of parameter changes on behaviors through bifurcation theory, identifies steady-state, periodic or chaotic states, and generates physiological dynamic diagnosis results;

[0064] The critical state interpretation module uses fuzzy logic method 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;

[0065] The process efficiency optimization module uses random forests 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;

[0066] The health trend prediction module is based on process optimization suggestions, using 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;

[0067] 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 variability sources and quantification of result fluctuations, and generates suggestions for improvement measures;

[0068] 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 analysis of interactions between features and model training, identifies and evaluates key information, and generates detailed interpretation results;

[0069] Based on the refined interpretation results, the potential pathology mining module uses cluster analysis and multivariate statistical analysis to identify patient groups with similar patterns from medical test results, analyze the groups and reveal potential health status or disease classification, and generate potential pathology analysis results;

[0070] 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 measures suggestions, detailed interpretation results, and potential pathology analysis results to evaluate and comprehensively judge the patient's health status, conduct comprehensive risk grading, and generate comprehensive health assessment results.

[0071] The results of physiological dynamic diagnosis include stability parameters, periodic fluctuation frequency, and chaotic dynamic characteristic values. The results of critical state analysis include fuzzy logic judgment level, boundary value sensitivity index, and state transition warning signal. Process optimization suggestions include key equipment scheduling priority, detection step simplification plan, and emergency response mechanism adjustment. Trend prediction results include health status evolution trend chart, abnormal health indicator list, and risk disease warning mark. Improvement measures include inspection accuracy optimization, data processing algorithm update, and experimental operation standardization process. Detailed interpretation results include key biomarker analysis, disease correlation score, and treatment response potential indicator. Potential pathology analysis results include unmanifested pathological state signals, group health trend deviations, and predictive disease classification maps. Comprehensive health assessment results include individual health comprehensive score, potential health risk level, and health management recommendations.

[0072] In the physiological dynamic identification module, the time series data of medical examinations are processed by adopting a nonlinear dynamic model. In this process, numerical integration techniques are used to simulate the dynamic behavior of the physiological system, and sensitivity analysis of parameter changes is performed in combination with attractor analysis and bifurcation theory. 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. The bifurcation theory is further used to analyze the behavioral changes of the physiological system under parameter changes, thereby identifying the transition points of the physiological state. This series of operations can generate physiological dynamic diagnosis results containing 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, the fuzzy logic method is used to construct a rule base and membership function to analyze and reason about the boundary values ​​and critical states in the test results. By defining the fuzzy set of each test indicator and the corresponding membership function, the fuzzy logic engine fuzzifies the physiological parameters according to the constructed rule base, and then performs reasoning analysis on the boundary values ​​and critical states, generating critical state analysis results such as fuzzy logic judgment level, boundary value sensitivity index, and state transition warning signal. These operations can provide doctors with more detailed and flexible health status assessments, which helps improve the accuracy of clinical decision-making.

[0074] In the process efficiency optimization module, random forest and neural network algorithms are used to analyze the inspection process data and identify potential bottlenecks and delays in the process. The random forest algorithm identifies the key factors affecting process efficiency by analyzing various indicators in the inspection process, such as sample processing time and equipment utilization efficiency; while the neural network algorithm predicts the delays in the process based on these data. Based on these prediction results, the linear programming method is used to adjust resource allocation and process settings, and generate process optimization suggestions, including key equipment scheduling priorities, inspection step simplification plans, and emergency response mechanism adjustments. This series of operations significantly improved the efficiency and response speed of the inspection process and reduced patient waiting time.

[0075] In the health trend prediction module, based on process optimization suggestions, 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 model (ARIMA) are used to predict long-term trends in health status, while clustering algorithms are used to identify abnormal health indicators and reveal sudden changes or potential risks in health status. Through these analyses, health status evolution trend charts, abnormal health indicator lists, and risk disease warning signs are generated to provide data support for individual health management and preventive medicine.

[0076] In the test accuracy improvement module, partial least squares regression and generalized additive models are used to analyze the variability of test results and identify the causes of inconsistent results based on trend prediction results. Partial least squares regression is used to analyze the complex relationship between test indicators and health status and identify the source of variability; while generalized additive models are used to quantify the fluctuation of test results and propose improvement measures such as test accuracy optimization, data processing algorithm update, and experimental operation standardization process. These analyses and suggestions help improve the accuracy and consistency of test results and reduce the misdiagnosis rate.

[0077] In the result interpretation optimization module, based on the improvement measures recommended, the conditional random field model is used to integrate custom feature extraction and prior knowledge to provide a detailed interpretation of the medical test results. The conditional random field model analyzes the features and their interactions in the test results, identifies and evaluates key information, and generates detailed interpretation results, including key biomarker analysis, disease association scores, and treatment response potential indicators. This process not only improves the accuracy of result interpretation, but also provides doctors 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 conditions or disease classifications. Cluster analysis reveals specific health conditions or disease risks by grouping patients with similar patterns; multivariate statistical analysis further analyzes the commonalities and differences between these groups, generating unrevealed pathology signals, group health trend deviations, and predictive disease classification maps. These analysis results provide an important basis for precision medicine and group health management.

[0079] In the comprehensive health assessment module, the analysis results of all the above modules are combined, and a comprehensive assessment algorithm is used to comprehensively assess and judge the patient's health status, and to perform comprehensive risk grading. Through the calculation of the comprehensive weighted scoring model, the multi-source data such as physiological dynamic diagnosis results, critical state analysis results, and process optimization suggestions are integrated to grade the patient's health risk and generate a comprehensive health assessment result including individual health comprehensive score, potential health risk level and health management suggestions. This comprehensive assessment provides patients with health management suggestions, which helps to improve health levels and prevent diseases.

[0080] See also 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 the time series data of medical examinations and is constructed using a nonlinear dynamic model. It uses the integrate module in Python's SciPy library for numerical integration. The parameters include time step and initial conditions. It simulates the dynamic nature 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.

[0082] The periodicity identification submodule uses Fourier transform to detect periodic fluctuations based on the results of the steady-state feature analysis. The transformation is performed through the fft function of the NumPy library. The parameters including sampling rate and data volume are set to find repetitive patterns or periodic fluctuations in the time series. The periodic properties of physiological activities are selected through spectrum analysis to generate periodic fluctuation detection results.

[0083] The chaotic state judgment submodule is based on the periodic fluctuation detection results and adopts the Lyapunov exponent to evaluate the chaotic state. The Lyapunov exponent is calculated using Python's Nolds library. The parameters include the embedding dimension and the data sequence. The sensitivity of the physiological process dynamics to the initial conditions and its unpredictability are evaluated. Through the calculation results of the Lyapunov exponent, it is determined whether the physiological activity exhibits chaotic characteristics and generates physiological dynamic diagnosis results.

[0084] In the steady-state analysis submodule, the time series data of medical examinations are analyzed by using a nonlinear dynamic model. The data format is in the form of a time series, recording the continuous observations of physiological activities. The integrate module in Python's SciPy library is used for numerical integration, where the parameter settings include time steps and initial conditions, which are used to simulate the dynamic properties of physiological systems. Through the analysis of attractor and bifurcation theory, the system can identify whether the physiological activity has reached a stable state, periodically fluctuated, or is in a chaotic state. The visualization of this process is achieved through the Matplotlib library, making the parameter changes and physiological state changes intuitive. Finally, this submodule generates the results of the stable state characteristic analysis, which records the stability parameters of physiological dynamics in detail, and provides basic data for the subsequent critical state interpretation.

[0085] In the periodicity identification submodule, Fourier transform is further used to detect periodic fluctuations based on the results of the steady-state feature analysis. The transformation is performed through the fast Fourier transform (fft) function of the NumPy library, where important parameter settings include sampling rate and data volume, which helps to identify repetitive patterns or periodic fluctuations in time series data. Spectral analysis is then used to determine the periodic properties of physiological activities, including the frequency and intensity of periodic fluctuations. Through these analyses, the submodule is able to generate periodic fluctuation detection results that clearly point out the periodic characteristics present in physiological activities, which is of great significance for understanding the regularity of physiological processes and predicting future trends.

[0086] In the chaos state judgment submodule, the Lyapunov exponent is used to evaluate the chaotic state of the physiological process based on the periodic fluctuation detection results. The Lyapunov exponent is calculated using the Python Nolds library. The key parameter settings include the embedding dimension and the data sequence length. These parameters help evaluate the sensitivity of the physiological process to the initial conditions and the degree of its unpredictability. The calculation results of the Lyapunov exponent help determine whether the physiological activity exhibits chaotic characteristics, thereby providing an accurate basis for identifying the nonlinear dynamics of the physiological system. Through the analysis of this submodule, physiological dynamic diagnosis results can be generated, which can not only identify the chaotic state, but also provide a scientific basis for further health status assessment and disease prevention.

[0087] Assume that in an application scenario of a test result evaluation system, it is necessary to analyze the electrocardiogram (ECG) data of a group of heart disease patients to identify the steady state, periodicity or chaos of the heart rhythm, and then evaluate the heart health of the patients. The ECG data items include time series data of the number of heart beats per minute, and the simulated values ​​are as follows: within 5 minutes, the number of heart beats is 72, 75, 73, 70, 68, 74, 77, 75, 76, 72, 78, 81, 79. In the steady-state analysis submodule, these data are analyzed using a nonlinear dynamic model, and the heartbeat stability parameters are determined by numerical integration and attractor analysis, such as the stable heartbeat range of 70-80 beats / minute. The periodicity identification submodule applies Fourier transform to identify the periodic fluctuations of the heartbeat data, such as the fluctuation frequency of the number of heart beats per minute is 1 beat / minute. The chaotic state judgment submodule calculates the Lyapunov index to evaluate the chaotic characteristics of the data. Assuming that the calculated Lyapunov index is a positive value, it indicates that the heart rhythm shows a chaotic state, suggesting that there is a risk of instability in the heart. 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. The membership function of the input variable is defined through the Scikit-Fuzzy library in Python. The membership function is set as a Gaussian distribution according to the distribution of medical data, and a rule library 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.

[0090] 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 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 under each physiological state is quantitatively assessed to generate risk assessment results for individual patients.

[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 by using the Scikit-Fuzzy library to process the physiological dynamic diagnosis results. The data format is numerical, representing the time series of physiological parameters. This submodule defines the membership function of the input variable, and selects Gaussian distribution as the form of the membership function because 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, and finally the fuzzy logic analysis results are generated, which 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 the decision tree model is constructed using the DecisionTreeClassifier in the Scikit-Learn library. In this process, the input data is the fuzzy value obtained by fuzzy logic analysis, including the fuzzy classification and membership information of each physiological parameter. The construction of the decision tree is based on the fuzzy value, and the maximum depth is limited to 5 layers to avoid the overfitting problem. Information gain is used as the node splitting criterion to optimize the structure of the decision tree. This process converts the fuzzy value into a specific risk level and generates risk assessment results for individual patients. These results quantitatively evaluate the risk under each physiological state and provide clear health risk guidance for doctors and patients.

[0094] In the critical state determination submodule, the PyKnow library is used to build an expert rule engine based on the risk assessment results of individual patients. This submodule integrates the scattered risk levels and determines the critical state through the rule base of the expert system. The input data is the risk assessment results of individual patients generated by the previous submodule, which includes the risk levels of various physiological states. The expert rule engine makes a critical state determination based on the set logical rules and comprehensively considers different risk levels, and re-evaluates the patient's current health status. The critical state analysis results finally generated provide a comprehensive assessment of the patient's health status, clearly point out the critical health status that requires special attention, and provide an important basis for clinical decision-making.

[0095] Assume that in a scenario of a test result evaluation system, a group of blood test data of patients need to be evaluated for health risks. This group of data items includes white blood cell count, hemoglobin value and blood sugar level. The simulated values ​​are as follows: white blood cell count is 6500 per cubic millimeter, hemoglobin value is 12 grams per deciliter, and blood sugar level is 5.5 millimoles per deciliter. In the critical state interpretation module, the fuzzy logic analysis submodule first defines the membership function of the input variable as Gaussian distribution based on these physiological dynamic diagnosis results, builds a rule base, converts clear physiological parameters into fuzzy values, and generates fuzzy logic analysis results. For example, the fuzzy value of blood sugar level is expressed as "normal", "high" or "high". The risk assessment submodule then performs risk assessment based on the fuzzy logic analysis results using the decision tree algorithm and builds a decision tree model to convert fuzzy values ​​into specific risk levels. For example, if the blood sugar level is "high", the patient's health risk level is set to "medium". The critical state determination submodule finally builds an expert rule engine based on the individual patient risk assessment results using the PyKnow library to generate critical state analysis results by integrating various risk levels. For example, by comprehensively considering the risk levels of white blood cell count, hemoglobin value, and blood sugar level, the patient's current health status is finally assessed as "needing further examination."

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

[0097] The bottleneck identification submodule uses the RandomForestClassifier of the Scikit-Learn library in the Python environment through the random forest algorithm based on the critical state analysis results. 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 with abnormal efficiency in the process 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 and builds a multi-stage feature including the input layer matching inspection process through the TensorFlow and Keras libraries. The hidden layer uses the ReLU activation function and the output layer uses the sigmoid function. The optimizer is set to Adam to optimize the model parameters. The loss function uses binary_crossentropy to predict the delays that occur at each stage based on the bottleneck analysis and generate delay prediction analysis results.

[0099] 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.

[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 abnormal efficiency links. 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, and max_depth is set to None, allowing the tree to grow to the maximum depth to obtain the best segmentation. 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 the bottlenecks and inefficient links in the inspection process in detail, providing a basis for process optimization.

[0101] In the delay prediction submodule, based on the bottleneck identification analysis results, a neural network algorithm is used to predict the delay of the inspection process. The neural network model built with TensorFlow and Keras libraries contains 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 nonlinear fitting ability of the model, and the output layer uses the sigmoid function to predict the probability of delay. The optimizer of the model is Adam, and the loss function uses binary_crossentropy to minimize the prediction error. By training the model, it is possible to predict the delays that 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. The linear programming method 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 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 generated process optimization recommendation report lists in detail the specific measures to improve the efficiency of the inspection process and reduce delays, and provides implementation suggestions for achieving process optimization and improving service efficiency.

[0103] Suppose that in a laboratory of a large hospital, the efficiency of the daily blood test process is optimized. The laboratory needs to process thousands of blood samples every day, involving multiple test items. In this scenario, the specific data items include: sample processing time (average processing time per sample is 15 minutes), equipment utilization rate (average daily utilization rate of some key equipment reaches 90%), and staff allocation (10 technicians per shift). The data is analyzed by the random forest algorithm, and the high utilization rate of equipment is identified as the main bottleneck, especially the delay caused by the high utilization rate of core analytical equipment. Then, the neural network model is used to predict the delay. The simulation value shows that during the peak period, the equipment utilization rate is as high as 95%, which is expected to cause at least 20% of the sample processing delays of more than 30 minutes. Based on this prediction, the resource allocation submodule takes action and recommends adding a piece of equipment of the same type and adjusting the work shifts of technicians to disperse the sample volume during the peak period. Finally, after implementing these optimization suggestions, the simulation results show that the utilization rate of the new equipment has dropped below 80%, and the average delay time of sample processing has been reduced to less than 10 minutes, which significantly improves the efficiency and responsiveness of the test 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 suggestions, builds an autoregressive integrated moving average model, defines the model's autoregressive term, difference order, and moving average term by setting the order parameter to the 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;

[0106] The outlier identification submodule is based on the time series trend graph and uses Python's scikit-learn library to implement the K-means algorithm. 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, and the data points with a distance greater than the specified threshold are identified as outliers, generating abnormal pattern recognition results.

[0107] The trend prediction submodule performs trend prediction based on the abnormal pattern recognition results. 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. It makes adjustments based on the impact of abnormal values ​​to generate trend prediction results.

[0108] In the time change analysis submodule, the autoregressive integrated moving average (ARIMA) model is used to process time series data, aiming to capture the trends and patterns of health indicators over time. The data format is a time-stamped sequence, for example, blood pressure measurements or blood sugar levels for consecutive weeks. The pandas library of Python is used for data preprocessing, such as filling missing values, converting timestamps, etc., and then the autoregressive integrated moving average model is constructed using the statsmodels library. The order parameters (p, d, q) of the model are set according to the autocorrelogram (ACF) and partial autocorrelogram (PACF) of the data to determine the optimal parameter combination of the model. After that, the model is fitted using the fit method, and the predicted trend graph of the time series is plotted by the plot_predict method. The time series change trend graph generated by this process reveals the evolution trend of health indicators over time, providing a visual basis for identifying long-term health changes.

[0109] In the outlier identification submodule, the 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 abnormal points that do not conform to the main trend. The K-means algorithm is implemented through Python's scikit-learn library, and the n_clusters parameter is set to determine the number of clusters, which is selected based on the distribution of the data and the expected type of abnormal patterns. The fit_predict method is used to perform cluster analysis on the time series data, and the data points with a distance greater than a certain threshold are marked as outliers by calculating the distance from each data point to its nearest cluster center. The abnormal pattern recognition results generated by this process reveal potential health risks or measurement errors, providing important information for further analysis.

[0110] In the trend prediction submodule, based on the abnormal pattern recognition results, the exponential smoothing method is used for trend prediction. This step uses the ExponentialSmoothing class in Python's SciPy library to optimize the model by setting seasonal adjustment parameters and smoothing parameters. The model uses the fit method to fit the data, and then applies the forecast method to predict future values. In this process, the impact of outliers is specially considered, and the prediction strategy is adjusted accordingly. The generated trend prediction results are presented in the form of charts, showing the future development trend of health indicators after considering the impact of outliers, providing patients with a prediction of future health status, helping doctors and patients make more informed health management and treatment decisions.

[0111] Assume that in a scenario where a hospital manages diabetes for patients, doctors need to evaluate the patient's blood sugar control trend and identify health risks. The specific data items include the average blood sugar measurement values ​​of a diabetic patient in 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, the ARIMA model is used to analyze these blood sugar values. The model parameters are set to (2,1,2), which represents the configuration of the autoregressive term, the difference order, and the moving average term. The time series prediction trend chart drawn after fitting the model shows that the blood sugar value has a gradual upward trend. The outlier identification submodule uses the K-means algorithm to set the number of clusters to 2 and analyzes the data. It is found that the blood sugar value of 9.0 mmol / L in the sixth month is much higher than that of other months and is marked as an outlier, suggesting that the patient's blood sugar control has problems. The trend prediction submodule uses the exponential smoothing method to predict the blood sugar levels for the next three months, taking into account the existence of abnormal values. The prediction results show that if no measures are taken, the patient's blood sugar level will continue to rise and reach more than 9.5 mmol / L.

[0112] See also Figure 2 and Figure 7 ,The test 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 Python's scikit-learn library and sets the number of components parameter n_components to 5 to perform fitting analysis on the data. It calculates the covariance between each variable and the response variable to identify the factors that contribute most to the variability of the test results, predicts and analyzes the data set, and generates variability source analysis results.

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

[0115] The improvement 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, and formulating improvement plans, including adjusting the standardized experimental operation process, and using data analysis technology to improve the consistency of results, and generates improvement proposals.

[0116] In the variability analysis submodule, the variability of the test results is deeply analyzed by partial least squares regression analysis. The data format used is multidimensional numerical data, in which each row represents a test result and the columns include various biochemical indicators. The PLSRegression class in the scikit-learn library of Python is used, and the number of components parameter n_components is set to 5, aiming 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 the accuracy of the test, but also provide a scientific basis for further optimizing the test process.

[0117] In the influencing factor identification submodule, based on the results of the variability source analysis, the generalized additive model (GAM) is applied to perform a more refined influencing factor analysis. This process is implemented through the Python pyGAM library, using the LinearGAM or GAM class for operation. The smoothing parameter splines is set to 20, and the n_splines parameter is automatically selected to adapt to the nonlinear characteristics of the data and reveal the complex relationship between the variability of the test results and the potential influencing factors. In this way, the model can select and quantify those explanatory variables that have a significant impact on the variability of the test results, and the generated influencing factor identification results provide accurate guidance for the formulation of targeted improvement measures.

[0118] In the improvement measures proposal submodule, a comprehensive review of the inspection process and data processing methods was conducted based on the results of the variability source analysis and the identification of influencing factors. By comprehensively considering the key influencing factors identified in the inspection process, a series of improvement measures were formulated, including adjusting the experimental design, optimizing the data processing algorithm, and adjusting the standardized process of experimental operations. In particular, data analysis techniques are used to improve the consistency of results, such as introducing more advanced data preprocessing and analysis methods to reduce the impact of external interference and operational errors on the inspection results. The generated improvement measures recommendation report lists in detail the implementation recommendations for each measure, with the aim of significantly improving the accuracy and reliability of medical tests through scientific data analysis and process optimization.

[0119] Suppose that in a medical research center, researchers are conducting a long-term follow-up study on the tumor marker levels of a group of patients. The goal is to identify factors that affect the variability of tumor markers and propose measures to improve diagnostic accuracy. Specific data items include the patient's 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 and whose tumor marker levels are: 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, analyzing and identifying the significant correlation between chemotherapy history and tumor marker levels, generating variability source analysis results, and revealing the main contribution of treatment history to tumor marker variability. The influencing factor identification submodule applies generalized additive model analysis, sets the smoothing parameter splines to 20, analyzes the nonlinear relationship between tumor marker levels and age and gender, identifies the impact of age as an important factor on tumor marker levels, and generates influencing factor identification results. Based on the above analysis results, the improvement measure proposal submodule proposes to adjust the inspection process and recommends adjusting the interpretation criteria of tumor markers based on the patient's treatment history and age information to improve the accuracy of diagnosis, and generates specific improvement measure proposal documents.

[0120] See also Figure 2 and Figure 8 ,The result interpretation optimization module includes the feature analysis optimization submodule, the knowledge integration submodule, and the interpretation accuracy improvement submodule;

[0121] The feature analysis optimization submodule performs feature analysis optimization of the conditional random field model based on the improvement measures suggested. Through Python's 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 through the training data set, and the fit method is used to analyze the features and their interactions in the medical test results, extract the key information related to the interpretation of the results, and generate feature analysis results.

[0122] The knowledge integration submodule integrates prior knowledge based on the feature analysis results. It adds prior knowledge in the medical field to the conditional random field model by setting a custom function, uses the information to optimize the recognition ability of the model, and generates knowledge integration results.

[0123] The interpretation accuracy optimization submodule optimizes the interpretation accuracy again based on the knowledge integration results. By optimizing the parameters of the conditional random field model, setting max_iterations = 1000 to enable the model to fully learn, and applying the predict method to interpret the new test results to generate refined interpretation results.

[0124] In the feature analysis optimization submodule, the interpretation accuracy of medical test results is improved through feature analysis optimization of the conditional random field (CRF) model. The data format covers various parameters of medical tests, such as biochemical indicators and clinical symbols, which are integrated into a training data set. Using Python's sklearn_crfsuite library, the parameter algorithm of the CRF model is set to lbfgs, and the regularization parameters c1 and c2 are set to 0.1 respectively. This setting helps prevent the model from overfitting while maintaining sufficient flexibility. Through the fit method, the model learns the interaction 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 diagnostic judgment, providing an accurate basis for subsequent interpretation.

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

[0126] Based on knowledge integration, the interpretation accuracy optimization submodule further optimizes the parameters of the conditional random field model to maximize the interpretation accuracy. By setting the max_iterations parameter to 1000, the model is ensured to fully iterate during the learning process to capture more subtle data patterns. The predict method is applied to interpret new medical test results. This step generates detailed interpretation results, which lists the diagnosis and associated medical information of each test result in detail. These detailed interpretation results provide doctors with more accurate diagnostic basis, which helps 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 examination. The simulated numerical example is a 56-year-old male patient with ALT of 45U / L, AST of 50U / L, ALP of 85U / L, and total bilirubin of 1.2mg / dL. Ultrasound examination showed mild fatty changes in the liver. In the feature analysis optimization submodule, the characteristics of these test results and their interactions are 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 in 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. The interpretation accuracy optimization submodule conducted deep learning on this basis, applied 1,000 iterations of the optimization process, and finally made a detailed interpretation of the patient's test results, predicting that the patient was at risk of moderate non-alcoholic fatty liver disease (NAFLD) and recommending further liver biopsy. The detailed interpretation results provide doctors with more accurate diagnostic basis, so that patients can receive targeted treatment recommendations, improve treatment effects and patient satisfaction.

[0128] See also Figure 2 and Fig. 9 ,The potential pathology mining module includes cluster recognition 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 executes the KMeans class through the Python scikit-learn library. It sets the n_clusters parameter to estimate the number of groups according to the 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.

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

[0131] The pathological pattern analysis submodule performs multivariate statistical analysis based on the health status classification results, including principal component analysis and linear discriminant analysis, through the scikit-learn library. The n_components parameter is set to select the number of principal components, revealing the pathological patterns among multiple health status classifications and generating potential pathological 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 includes multidimensional test results of patients, such as blood, urine, and imaging test data, which are converted into numerical data sets for algorithm processing. The KMeans class is implemented using Python's scikit-learn library, and the n_clusters parameter is set based on the number of groups estimated based on data characteristics, and the data is processed using the fit_predict method. The cluster grouping results generated by this process help identify groups of patients with similar health problems or pathological conditions, providing a basis for targeted treatment and further research.

[0133] In the health status classification submodule, the decision tree algorithm is used to classify the patient's health status based on the clustering grouping results. The data format is the same as the group label after cluster analysis, combined with the patient's specific health information, such as diagnosis results and treatment response. The decision tree model is implemented through the DecisionTreeClassifier class of the scikit-learn library. The adjustment of the max_depth parameter is intended to prevent overfitting and ensure the generalization ability of the model. Through this step, the generated health status classification results divide patients into different health status levels or pathology types in a more detailed manner, providing doctors with a more accurate diagnostic reference.

[0134] The pathological pattern analysis submodule uses multivariate statistical analysis methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), to perform in-depth analysis based on the health state classification results. The data format processed by these methods is a high-dimensional set of patient features, including 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 the pathological patterns and associations between different health state classifications, and the generated potential pathological 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 that in a large medical center, the research team is conducting a study on a group of patients with cardiovascular disease, with the goal of discovering potential 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 sugar levels, and electrocardiogram results. The simulated data example includes a batch of patient data, such as a 56-year-old male patient with blood pressure of 145 / 90mmHg, total cholesterol of 220mg / dL, blood sugar level of 5.6mmol / L, and an electrocardiogram showing mild ST segment elevation. In the cluster identification submodule, these data are processed by the K-means algorithm. Assuming that the number of groups estimated based on the characteristics of the data is 3, the cluster analysis reveals three patient groups with similar medical test result patterns, reflecting different degrees of cardiovascular disease risk. In the health status classification submodule, based on the clustering results, the decision tree algorithm is used to further classify these groups into three health states: "low risk", "medium risk", and "high risk". By setting max_depth to 4, overfitting is avoided while ensuring the accuracy of classification. The pathological pattern analysis submodule uses principal component analysis and linear discriminant analysis to analyze the main components and pathological patterns of different health status groups, revealing that hypertension and high cholesterol are the main risk factors for high-risk groups for cardiovascular disease. The potential pathological analysis results provide new insights into the prevention and treatment of cardiovascular disease, especially in the design of intervention measures for high-risk patient groups.

[0136] See also Figure 2 and Fig.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 assess the patient's health status based on physiological dynamic diagnosis results, critical state analysis results, process optimization suggestions, trend prediction results, improvement measures, detailed interpretation results, and potential pathology analysis results. It combines multi-source data through a comprehensive weighted scoring model, and the parameters include the impact weight of each data source. The patient's health risk is graded to 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 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 items, predict future changes in health status, and generate health trend forecast analysis results.

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

[0140] The comprehensive risk assessment submodule uses a comprehensive assessment algorithm to analyze data from different submodules, including physiological dynamic diagnosis, critical state analysis, process optimization suggestions, trend prediction, improvement measures, detailed interpretation results, and potential pathology analysis results. These data sources are integrated and processed through a weighted scoring model, in which the influence weight of each data source is carefully adjusted to ensure the accuracy and reliability of the assessment results. The process uses an algorithm to automatically classify the health risks of patients and generates a comprehensive rating report containing risk levels, which provides an important basis for medical decision-making.

[0141] The trend comprehensive analysis submodule uses the ARIMA model of time series analysis and trend forecasting to predict the long-term trend of health data. By accurately setting model parameters, such as the difference level and the number of autoregressive terms, this module can capture the dynamic trend of health status over time and generate a detailed health trend forecast report based on it. This report not only depicts the changes in the patient's future health status, but also provides a scientific basis for preventive health interventions.

[0142] The pathology comprehensive judgment submodule uses logistic regression analysis to evaluate the probability of a specific pathology developing based on the health trend prediction analysis results and potential pathology analysis results. This process takes into account the patient's health data and potential pathology characteristics, accurately adjusts the parameters of the regression model, and finally generates a detailed pathology comprehensive judgment report. This report provides doctors with in-depth insights into the development of patients' pathological states, helping them make more accurate medical decisions.

[0143] Assume that in a study on cardiovascular disease risk assessment, a test result evaluation system is used to analyze and predict the patient's health risk. The data items include but are not limited to the patient's blood pressure readings, cholesterol levels, glycosylated hemoglobin ratio, body mass index (BMI), and historical cardiovascular event records. The specific simulation values ​​are as follows: blood pressure reading 120 / 80mmHg, cholesterol level 200mg / dL, glycosylated hemoglobin ratio 5.6%, body mass index 25kg / m 2 , no history of cardiovascular events. Through the comprehensive evaluation algorithm, these data are comprehensively considered, and the influence weights of each data item are adjusted to ensure the accuracy of the evaluation results. For example, the influence weight of cholesterol level is 0.3, the influence weight of blood pressure reading is 0.25, the influence weight of body mass index is 0.2, the influence weight of glycated hemoglobin ratio is 0.15, and the influence weight of historical cardiovascular event record is 0.1. After these data are processed by the weighted scoring model, the patient is assigned to the low-risk group, and the comprehensive risk rating result is shown as "low risk", with a specific score of 78 points (out of 100 points). The trend comprehensive analysis submodule uses the ARIMA model with parameters set to (2,1,2), reflecting the number of autoregressive items, difference levels, and moving average items of the patient's health data. Based on these parameters, the model predicts that the patient's health status will remain stable in the next 12 months, and no significant trend of health deterioration is shown. The pathological comprehensive judgment submodule evaluates the probability of the patient developing cardiovascular disease in the future as 15% through logistic regression analysis, indicating that the patient has a low risk of developing cardiovascular disease under the current health status and lifestyle.

[0144] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0145] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. 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 the time series data, analyzes parameter changes, and generates physiological dynamic diagnosis results; The critical state interpretation module analyzes the boundary value of the test result based on the physiological dynamic diagnosis result to generate a critical state analysis result; 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 suggestions; The result interpretation optimization module interprets the medical test results based on the improvement measures proposed, 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.

2. The test result evaluation system according to claim 1, characterized in that: 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 deviations, and predictive disease classification maps. The comprehensive health assessment results include individual health comprehensive scores, potential health risk levels, and health management suggestions.

3. The test result evaluation system according to claim 1, characterized in that: 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 constructed based on the time series data of medical examinations using a nonlinear dynamic model, and uses the integrate module in Python's SciPy library for numerical integration. The parameters include time step and initial conditions, simulate the dynamic properties of physiological activities, analyze the attractor and bifurcation theory, and use the Matplotlib library to graphically display parameter changes, identify whether the physiological activity has reached a stable state, and generate stable state characteristic analysis results; The periodicity identification submodule applies Fourier transform to detect periodic fluctuations again based on the stable state feature analysis results, performs the transformation through the fft function of the NumPy library, sets parameters including sampling rate and data volume, finds repetitive patterns or periodic fluctuations in the time series, selects the periodic properties of physiological activities through spectrum analysis, and generates periodic fluctuation detection results; The chaotic state judgment submodule is based on the periodic fluctuation detection results, uses the Lyapunov exponent to evaluate the chaotic state, and uses Python's Nolds library to calculate the Lyapunov exponent. The parameters include the embedding dimension and the data sequence, and evaluate the sensitivity of the physiological process dynamics to the initial conditions and its unpredictability. Through the calculation results of the Lyapunov exponent, it is determined whether the physiological activity exhibits chaotic characteristics and generates physiological dynamic diagnosis results.

4. The test result evaluation system according to claim 1, characterized in that: 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. The membership function of the input variable is defined through the Scikit-Fuzzy library in Python. The membership function is set as a Gaussian distribution according to the distribution of medical data, and a rule library 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 is based on the fuzzy logic analysis results, adopts the decision tree algorithm, and is executed through the DecisionTreeClassifier function in the Scikit-Learn library. The decision tree is constructed based on the fuzzy value, and the maximum depth is set to 5 layers to avoid overfitting. Information gain is used as the node splitting criterion to convert the fuzzy value into a risk level. According to the fuzzy classification provided by the fuzzy logic, the risk under each physiological state is quantitatively evaluated to generate the risk assessment results of 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.

5. 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 is based on the critical state analysis results, and uses the RandomForestClassifier of the Scikit-Learn library in the Python environment through the random forest algorithm. 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 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, uses a neural network algorithm, and builds a multi-stage feature including an input layer matching inspection process through TensorFlow and Keras libraries. The hidden layer uses the ReLU activation function, the output layer uses the sigmoid function, and the optimizer is set to Adam to optimize the model parameters. The loss function uses binary_crossentropy to predict the delay that occurs at each stage based on the bottleneck analysis and generate a delay prediction analysis result; The resource allocation submodule performs resource allocation based on the delay prediction analysis results, 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, and adjusts the resource allocation strategy and process settings to generate process optimization suggestions.

6. The test result evaluation system according to claim 1, characterized in that: The health trend prediction module includes a time change analysis submodule, an abnormal value 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, builds an autoregressive integrated moving average model, defines the autoregressive term, difference order, and moving average term of the model by setting the order parameter to the 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 is based on the time series change trend graph, uses Python's scikit-learn library to implement the K-means algorithm, defines the number of clusters by setting the n_clusters parameter, uses the fit_predict method to perform cluster analysis on the data, uses the distance metric to identify the distance between each data point and its nearest cluster center, identifies data points whose distance is 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 for 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.

7. The test result evaluation system according to claim 1, characterized in that: The inspection 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, uses the PLSRegression class in the scikit-learn library of Python, sets the number of components parameter n_components to 5, performs fitting analysis on the data, calculates the covariance between each variable and the response variable, 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 variability source analysis results, applies generalized additive model analysis, uses Python's pyGAM library, operates through LinearGAM or GAM class, sets the smoothing parameter splines to 20, and the n_splines parameter to automatic selection, analyzes the nonlinear relationship between the variability of the test results and the potential influencing factors, including selecting the response variable and the explanatory variable, selecting the importance of the explanatory variable through statistical methods, and generating 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, and formulating improvement plans, including adjusting the standardized experimental operation process, and using data analysis technology to improve the consistency of results, and generates improvement measure suggestions.

8. 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 optimization submodule performs feature analysis optimization of the conditional random field model based on the improvement measures suggested. Through 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 through the training data set, and the features and their interactions in the medical test results are analyzed using the fit method, and the key information related to the interpretation of the results is extracted to generate 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, optimizes the recognition ability of the model using information, and generates knowledge integration results; The interpretation accuracy optimization submodule further optimizes the interpretation accuracy based on the knowledge integration result, 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.

9. 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, adopts the K-means algorithm, executes the KMeans class through the Python scikit-learn library, sets the n_clusters parameter to estimate the number of groups according to the data characteristics, and uses the fit_predict method to cluster the medical test result data, identifies the patient groups with similar patterns, and generates 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 classifies the clustering results into health status to generate health status classification results; The pathological pattern analysis submodule performs multivariate statistical analysis, including principal component analysis and linear discriminant analysis, based on the health status classification results through the scikit-learn library, setting the n_components parameter to select the number of principal components, revealing the pathological patterns among multiple health status classifications, and generating potential pathological analysis results.

10. 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 and calculates through a comprehensive weighted scoring model. The parameters include the influence weight of each data source, and the patient's health risk is graded to 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 items, 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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