614 results about "Physical examination" patented technology

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses an intelligent physical examination data quality control analysis method and system, and relates to the technical field of medical quality control. The problems that existing physical examination data are diversified in source, complex in format, single in quality control means and the like are solved. Structured data, medical images and texts are integrated, and standardized processing and data mapping are achieved through a unified platform. Static and dynamic rule libraries are constructed, and the threshold is dynamically adjusted in combination with individual features, so that personalized risk assessment is realized. Structured data anomaly detection is performed by adopting machine learning, an image quality problem is identified by utilizing a convolutional neural network, and text anomaly is processed and analyzed through a natural language, so that the anomaly detection accuracy is improved. The system realizes multi-dimensional quality control and intelligent early warning, enhances the integrity and credibility of physical examination data, supports precise health management and disease early warning, improves the quality control efficiency, meets the intelligent analysis requirements of large-scale multi-modal physical examination data, and promotes the application of intelligent health management.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

The invention discloses an intelligent inspection method for a special operation physical examination room, and relates to the field of path planning, and the method comprises the steps: obtaining examination room data and environment parameters in real time, and building a live-action navigation map based on the examination room data, the live-action navigation map being used for real-time linkage with the examination room data; acquiring an examination room image in real time through inspection equipment; image features of the examination room image are extracted, the inspection path is divided into n areas based on the image features, and n is a positive integer; executing a dynamic path planning strategy based on the live-action navigation map and the image features to generate an inspection path of the inspection equipment; performing fusion analysis on the environmental parameters and the image features to determine a congestion index on the inspection path; and determining a dangerous area in the n areas based on the congestion index, and visualizing the dangerous area. The inspection accuracy of the special operation physical examination room can be effectively improved, so that the risk hidden danger of the examination room is effectively reduced.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention discloses a cancer early warning management method and system based on a physical examination report, and belongs to the field of medicines.The method comprises the steps that health data of individuals are collected, and unstructured texts in the physical examination report are processed through combination of optical character recognition and a natural language processing technology; calculating a cancer risk based on a single physical examination report, evaluating a specific cancer by using a cancer risk scoring system, and analyzing whether an index exceeds a normal range or not through a rule engine; based on historical data trend prediction risks, index change rates are calculated, and an abnormal trend is analyzed and predicted in combination with a time sequence; multi-modal deep learning is utilized to predict individual cancer risks, and a Bayesian network is combined to perform joint analysis on multiple factors to generate a health management scheme; further examination is arranged according to the early warning level, and a screening strategy is provided for specific cancers. According to the method, the OCR + NLP technology is adopted, and the data quality is improved. And in combination with a multi-modal deep learning model, abnormal changes are found in advance, and early warning is realized.

The invention discloses a health examination data analysis method and system based on artificial intelligence, and the method comprises the steps: receiving multi-modal health examination original data of a user, and generating a multi-modal feature matrix fused with time-space correlation; inputting the multi-modal feature matrix into a lightweight dual-channel network to obtain a decoupled dual-modal feature group; constructing a hidden Markov chain based on the bimodal feature group, and generating a health state transition trajectory diagram with a timestamp; inputting the health state transition trajectory diagram into a discriminator of the generative adversarial network, and outputting a confidence score and a pathology trigger threshold of a high-risk node; and activating a rule engine according to a pathological trigger threshold, and generating a hierarchical health intervention instruction set in combination with individual living habit data. By using the embodiment of the invention, the physical examination data can be efficiently analyzed, and the accuracy of disease early warning and health intervention is optimized.

The invention relates to the technical field of flight ability evaluation, in particular to a pilot emergency processing ability evaluation method and system fused with physiological signals. Initial ability indexes are extracted based on literature research and physical examination standards, and physiological data of a pilot in a normal state and a simulated emergency state are collected; the method comprises the following steps: extracting potential ability indexes through dimensionality reduction and denoising of a component analysis algorithm and factor analysis (FA), and screening remarkably changing physiological indexes (plt; 0.05), and performing correlation combination with the initial index (a correlation coefficient gt; 0.8) constructing an emergency capability evaluation index system including situation awareness, decision making, communication, pressure management, team cooperation, reaction time, strain and flexibility; the pilot emergency ability assessment method and system are constructed through multi-source physiological signal fusion and dynamic modeling, accurate quantitative analysis and personalized training guidance are achieved, and civil aviation safety specifications are adapted.

The invention relates to the technical field of artificial intelligence, is suitable for financial and medical scenes, and particularly relates to a low-illumination physical examination image noise reduction enhancement method, device and equipment and a storage medium, the low-illumination physical examination image noise reduction enhancement method comprises the following steps: obtaining a to-be-processed low-illumination physical examination image; based on a Retinex decomposition model, acquiring a preliminarily estimated illumination component of the low-illumination physical examination image, acquiring an accurately estimated illumination image according to the preliminarily estimated illumination component, and enhancing the accurately estimated illumination image through gamma transformation to obtain an enhanced image; performing noise reduction on the enhanced image based on a BM3D algorithm to obtain a noise-reduced image; and combining the enhanced image and the noise reduction image to obtain a noise reduction enhanced image. The problem that in the prior art, when enhancement processing is carried out on the low-illumination physical examination image through an image enhancement algorithm, synchronous processing is not carried out on noise and amplified noise in the enhanced physical examination image is solved, and the accuracy and efficiency of physical examination image recognition are greatly improved.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

The invention relates to the technical field of mining hydraulic support detection, in particular to a method for detecting the sealing performance of a piston rod cavity after overhaul of a mining hydraulic support stand column, and the method comprises the following steps: S1, preparation before detection, S2, equipment connection, S3, low-pressure performance test, S4, high-pressure performance test, S5, processing after detection, and S6, report generation. According to the invention, a multi-stage segmented detection mode is adopted, a low-pressure performance test is firstly carried out, just like a primary physical examination is carried out on the stand columns, so that the stand columns with sealing hidden dangers in a low-pressure environment can be preliminarily screened out, a high-pressure performance test is carried out after the stand columns are qualified, and the sealing performance of the stand columns is greatly improved through a test mode of gradually increasing the pressure in a segmented manner. Therefore, stable pressure change is ensured, the performance of the sealing performance of the stand column under different pressure conditions can be found more accurately, and the detection method can accurately and efficiently detect the sealing performance of the piston rod cavity after the mining hydraulic support stand column is overhauled through a comprehensive detection process and reasonable parameter control.

The invention provides a physical examination data exception association analysis method and system based on a knowledge graph, and relates to the technical field of exception association analysis, and the method comprises the steps: determining all physical examination items of a target physical examination center; aiming at a plurality of selectable physical examination indexes, executing abnormal generality association and generality confidence degree analysis among the indexes, and establishing an abnormal generality association map; receiving a physical examination report; executing abnormal generality association verification based on the abnormal generality association graph; performing abnormal distribution characteristic analysis in a preset period for the plurality of selectable physical examination indexes, and performing distribution abnormality verification on the physical examination report; and performing joint abnormity verification according to the distribution abnormity verification result and the generality verification result, then performing abnormal physical examination item positioning, and sending the abnormal physical examination item positioning to a target physical examination center for reminding. The technical problem that potential health risks are difficult to recognize in time and the accuracy of physical examination results is affected in the prior art can be solved, and the technical effect of improving the accuracy of physical examination anomaly detection is achieved.

According to the multi-mode speech recognition and structured electronic medical record generation system based on environmental perception, the dialogue content between a doctor and a physical examination person and the oral instruction / result of the doctor are accurately captured in a non-inductive mode and are intelligently filled into the predefined structured electronic medical record template of the corresponding department, so that the manual input time of the doctor is shortened, and the efficiency is improved. The system liberates both hands and eyes, focuses on inquiry and examination, improves the physical examination quality, and improves the satisfaction degree of physical examinees.

A mine earthquake adaptive positioning method and system based on multi-index fusion discrimination belong to the field of mine earthquake positioning, and the system comprises a data acquisition module, a feature extraction and quality evaluation module, a comprehensive index calculation module and an adaptive decision module. The method comprises the following steps: firstly, carrying out multi-dimensional physical examination on input waveform data, station layout and a speed model by the system, and generating a quantized physical examination report comprising a signal-to-noise ratio, a pickup error, station geometric coverage, model credibility and the like, namely a multi-dimensional feature vector; and then, a built-in rule engine automatically screens out an optimal single method or combined strategy under the current condition from multiple positioning methods according to the report and a demand target set by a user, and outputs a decision result with a clear basis. Through the system and the method, full-process automation from data input to strategy output is realized, and efficient and reliable technical support is provided for mine safety monitoring and disaster early warning.

The invention discloses a health early warning management system and method based on physical examination information, and particularly relates to the field of skeleton structure recognition, and the method comprises the steps: constructing a structural posture imaging data set based on a physical examination posture image of a student, extracting three-dimensional structure coordinates of parts, such as a scapula, a spine and a pelvis; and uniformly mapping to a skeleton reference system to calculate a body inclination index, a spine offset rate and a pelvic inclination index. Then, constructing a time sequence of posture structure parameters, and generating a trend feature vector sequence according to academic difference and gradient extraction; and further constructing a posture structure evolution trajectory diagram by taking the structure parameters as nodes and the trend vectors as edge attributes, and capturing a cross-year posture change path of the students. On the basis, all paths are aligned according to ages, the sudden change trend is extracted, the posture deviation high-incidence risk of a specific age group is recognized, and accurate student posture early warning management is achieved.

The invention provides a three-dimensional five-axis laser cutting intelligent equipment state monitoring method, a medium and equipment, and relates to the technical field of machine tool state monitoring, the method comprises the following steps: training an abnormal point identification model based on offline physical examination current data to obtain an abnormal point identification model of a scene; collecting current data and motion signal data of the corresponding shaft; inputting the segmented current data into an abnormal point identification model to capture abnormal points, and calculating an abnormal score based on model evaluation; visualizing the segmented and sliced current data to obtain a current picture, and calculating the similarity between the current picture and the target data picture; and carrying out weighted calculation on the abnormal scores and the similarity, calculating a health state score of each segment, and determining abnormal segments according to the health state scores so as to locate a fault position. According to the method, the conditions of the ABCXYZ axes of the equipment can be accurately monitored under the condition that a sensor and annotation data do not need to be additionally added.

A diagnostic device is configured to capture diagnostic information about a subject (e.g., the subject's ears). The diagnostic device may include a diagnostic extension housing diagnostic elements (e.g., imaging chip, light sources, or lenses) and an ear canal engagement section coupled to the diagnostic extension. The ear canal engagement section may be shaped to position the diagnostic extension superiorly within the ear canal. For example, one side of the ear canal engagement section may be longer than another, which may enable the ear canal engagement section to contact the inferior wall of the ear canal for a longer distance than it contacts the superior wall. This configuration may allow more movement of the diagnostic extension when the device is inserted into the ear, which further improves the alignment of diagnostic elements with a desired area of the ear for diagnoses (e.g., the eardrum).

The invention discloses a health analysis report generation method based on a large model. The report generation method comprises the following steps: inputting a physical examination report; preprocessing the physical examination report to obtain a preprocessing result; performing visual language model analysis according to the preprocessing result; carrying out structured processing; compressing the cue word; generating an HTML (Hypertext Markup Language): generating webpage contents by utilizing And rendering and exporting. By introducing a large model with natural language understanding, data reasoning and code generation capabilities, an intelligent report generation mechanism is constructed, while the system development workload is reduced and the development period is shortened, specialized, precise and personalized output of health reports is realized, and the scientificity, readability and practical value of report contents are improved.

The invention relates to the technical field of medical data processing, and discloses a disease risk assessment method based on a disease risk knowledge graph, the disease risk knowledge graph comprises a disease risk rule and a disease knowledge entity, and the disease knowledge entity comprises a focus position entity; performing organ positioning according to the physical examination data of the target object, and determining a target organ; performing organ matching and index data matching in a disease risk rule based on the target organ and the physical examination data to obtain a risk matching rule corresponding to the target organ; performing disease risk level analysis on the target organ according to the risk matching rule to obtain a disease risk level of the target object about the target organ; generating a physical examination report of the target object according to the physical examination data, the disease risk level and the target focus position; wherein the physical examination report comprises a three-dimensional model used for displaying the target focus position. The method has the beneficial effects that the pertinence and efficiency of disease risk assessment are improved, and the target object is helped to understand the health state of the target object.

The embodiment of the invention discloses a physical examination report standardized storage method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: carrying out image enhancement processing on an obtained user physical examination report image set, and then carrying out physical examination item classification processing to obtain a first physical examination item report image set and a second physical examination item report image set; generating an image detection text information set; performing image text recognition on the user physical examination text region image set to obtain a physical examination report text recognition information set; performing text calibration on the physical examination report text identification information set to obtain a physical examination report text calibration information set; generating a report standardized prompt word group set; performing content extraction on the physical examination report text calibration information set to obtain a report extraction information set; and matching and storing the report extraction information set to a user physical examination report storage system. According to the embodiment, the extraction accuracy and effect of the physical examination report information can be improved, the extraction time is shortened, and waste of storage resources is reduced.

The present invention relates to the field of biomedical technology, and more specifically to a method and system for assisting in predicting the risk of premature birth during pregnancy. The method comprises obtaining the physical data and delivery cycles of historical pregnant women, determining the degree of premature birth of the historical pregnant women based on the delivery cycles, and determining the degree of premature birth correlation of different types of physical data of the historical pregnant women based on the differences in the degree of premature birth between different historical pregnant women and the differences in the deviation changes of the same type of physical data during each physical examination. The historical pregnant women are clustered based on the deviation time differences of the same type of physical data between different historical pregnant women, and the overall correlation of each type of physical data is determined based on the number of pregnant women in the cluster and the degree of premature birth correlation of the corresponding type of physical data, thereby screening out the physical data of the target type. A premature birth prediction model is constructed based on the physical data of the target type during different physical examinations of the historical pregnant women to perform premature birth prediction. The present invention effectively improves the accuracy of premature birth prediction.

The invention relates to the technical field of health management, in particular to a health management physical examination big data platform which comprises a fluctuation starting point positioning module, a rhythm structure adjusting module, a trend triggering recognition module, a physical sign linkage extraction module and an intervention task sorting module. According to the method, the fluctuation starting point is positioned through the combination of the resistance change direction and the offset mutation, the sampling interval is dynamically adjusted, the fluctuation sequence is rearranged, the trend continuation path is recognized through the boundary deviation state, the continuity and the stability of trend recognition are enhanced, and the response starting point and the continuation area are extracted in combination with the direction consistency item in the respiratory signs; the integrity of time domain response coverage is ensured, a priority calling sequence is further constructed according to the distribution density, the occurrence frequency and the duration of the signs on the time axis, and accurate identification of a trend starting point, orderly adjustment of a rhythm structure and optimization of an intervention task time sequence in the multi-sign dynamic linkage process are achieved; and the response efficiency and the control precision in the health state evolution process are improved.

The invention discloses a physical examination guiding method based on intelligent wearable equipment, electronic equipment and a storage medium. The physical examination guiding method comprises the following steps: acquiring physical examination item information; establishing and keeping communication connection with the intelligent wearable device, and sending the physical examination item information to the intelligent wearable device; obtaining an electronic three-dimensional map, and department position information, average time consumption information and current people number information of each physical examination item; according to the current people number information of all the physical examination items, the average time consumption information of the uncompleted items and the current people number information, obtaining predicted waiting time through a time calculation model; and receiving the current position information from the intelligent wearable device, generating a physical examination guide route through a route planning model based on the electronic three-dimensional map according to the current position information and the department position information corresponding to the unfinished item with the shortest predicted waiting time, and sending the physical examination guide route to the intelligent wearable device. Real-time and accurate physical examination guidance can be provided for physical examinees, the physical examination process is optimized, and the physical examination efficiency is improved.

The invention relates to the technical field of child health assessment, in particular to a child body fat rate adaptive prediction method and system based on trajectory feature collaboration.The method comprises the following steps that a child physical examination data set is obtained, height and weight changes are analyzed, difference ratio screening samples are calculated, waistline change slope assessment stability screening samples are extracted, and a child body fat rate prediction result is obtained; calculating a sign proportion difference to adjust a training weight, identifying an age group residual difference to adjust model parameters, and adjusting a prediction slope generation result in combination with a body fat trend direction and a body weight trend direction. According to the method, samples are screened through height and weight tracks, abnormal data are eliminated in combination with fluctuation continuity of waistline change slope, a feature weighting coefficient is set according to proportion deviation of height, weight and muscle amount, and a response coefficient is adjusted through a distance relation between a grouping mean value of age group residual errors and a concentration interval. And the change path is judged by utilizing the consistency of the body fat and body weight prediction trend, so that non-trend prediction offset is avoided, and the stability of the model and the sample adaptability are improved.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a human body state characteristic value analysis method, device and equipment and a medium. Performing system state analysis on an endocrine system, a circulatory system and a respiratory system of the user according to the physical examination data to obtain a system state characteristic value set, constructing a physiological index vector and a disease course characteristic vector according to the physical examination data, and constructing a complication incidence matrix according to case data; performing complication association analysis on the user according to the physiological index vector, the disease course feature vector and the complication association matrix to obtain a complication feature value, analyzing a criticality feature value of the user according to a system state feature value set and the complication feature value to obtain a hazard feature value set, and performing weighted summation on the hazard feature value set to obtain a complication feature value set; and obtaining a human body state characteristic value. And the accuracy of human body state characteristic value analysis is improved.