266 results about "Risk factor" patented technology

The invention relates to the technical field of health monitoring, in particular to a cardiovascular disease data fusion and multi-dimensional health assessment method, which comprises the following steps of: acquiring multi-source data of a patient through a sensor and wearable equipment, preprocessing the acquired data, ensuring data integrity, extracting physiological features by adopting a convolutional neural network, and evaluating the health of the patient. The method comprises the steps of extracting non-physiological data features through principal component analysis, constructing a multi-dimensional feature set, identifying cardiovascular disease related risk factors through a random forest algorithm, distributing weights through a logistic regression algorithm to generate individualized risk scores, analyzing time sequence trends of the risk factors based on a long and short-term memory network model, and generating dynamic risk scores in real time. When the score exceeds a preset threshold value, an early warning mechanism is automatically triggered, and a patient and medical staff are notified through an application program; according to the method, accurate assessment and personalized intervention of cardiovascular health are realized through multi-source data fusion and dynamic analysis, and the method has high efficiency and timeliness.

The invention relates to a food safety risk analysis method and system based on big data. The method comprises the steps of collecting food full-life-cycle key data through Internet of Things equipment and a data platform, and completing data format standardization to form a structured data set; key risk factors are extracted through the structured data set, a risk factor weight matrix is constructed, and a primary risk value of the food sample is calculated; establishing a time sequence prediction model by using the primary risk value and historical time sequence data, calculating a risk change trend, and predicting a future risk fluctuation condition; calculating a food risk grade according to the primary risk value and the risk change trend, and generating a corresponding risk classification label; performing comparative analysis on the risk classification label and an actual supervision result, calculating a model deviation, and optimizing a risk calculation and trend prediction model based on a deviation result; the optimized analysis results are integrated, the risk level and the change trend are displayed through a graphical interface, and intelligent early warning prompts and intervention decision suggestions are provided.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention discloses a personal obesity risk prediction system and method based on AI of big data, and belongs to the field of medical health, and the system comprises a data collection module, a multi-dimensional feature construction module, a risk label dynamic generation module, a model training and risk prediction module, a credibility evaluation and calibration module and the like. The system collects multi-source heterogeneous data through wearable equipment, a biochemical interface and a health platform API (Application Program Interface), uniformly encodes the multi-source heterogeneous data into a standard time sequence and then constructs behavior-metabolism-environment coupling characteristics. And performing joint modeling on the dynamic features and the labels by adopting a graph neural network in combination with causal factorization, and outputting an individual obesity risk prediction result. The result credibility is improved through Monte Carlo Dropout and a confidence interval calibration mechanism, and calibration information is fed back to a feature construction link to optimize a modeling strategy. Finally, the key risk factors are presented in the form of a visual thermodynamic diagram and a causal path diagram, and an individualized intervention suggestion vector is generated. The method has the beneficial effects of improving prediction accuracy and enhancing individual intervention pertinence.

The invention relates to the technical field of information platform construction, in particular to a construction method and system of a sports information intelligent service platform. The method comprises the following steps: collecting user motion image data and user health basic data; key body node position information in the movement process of the user is extracted; generating a user motion track curve based on the key node position information, and calculating a deviation angle and a deviation distance with a standard track template to obtain motion correctness data; constructing a user fitness risk assessment model according to the action correctness data and the user health basic data, analyzing the difference degree with a preset safety threshold, and generating layered health guidance data; and decomposing the layered health guidance data into quantifiable progress units, and generating progress tracking data. According to the method, the user motion image data and the health basic data are combined, the evaluation model capable of identifying individualized risk factors is constructed, and more accurate safety guidance is realized.

The invention relates to the technical field of risk management, in particular to a water conservancy project risk early warning analysis method and system based on multi-source information, and the method comprises the following steps: dividing a dam body, a flood discharge gate and a spillway region according to spatial dimensions, extracting leakage monitoring records of a flood season, a dry season and an emergency weather event period, and storing the leakage monitoring records; and calculating a trigger factor frequency to generate a trigger factor feature sequence. According to the method, through hierarchical division of space and time dimensions, extraction of trigger factor frequency and analysis of support degree and confidence degree are combined, so that extraction of risk factor features is more comprehensive and accurate. In the construction of the dynamic propagation path, the spatial trajectory and the time evolution rule of the risk factor are deeply analyzed, so that the propagation path of the risk factor is clearly displayed, the accurate prediction of the risk diffusion region is realized, and a basis is provided for the division of the risk level in the subsequent region.

Methods for training and using a machine learning model for predicting a likelihood of an event occurring in a patient having a malignant tumour are provided. The method of using the machine learning model includes inputting a pathology image of a portion of a malignant tumour of a patient and one or more categorical risk factors associated with the malignant tumour or the patient. The method also includes extracting, from the pathology image, one or more features relating to one or more properties of the malignant tumour. The method further comprises determining a predicted likelihood of an event occurring in the patient based on the extracted features and the one or more categorical risk factors. The method additionally includes outputting the predicted likelihood.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention discloses a constipation prediction system based on an artificial intelligence large model, and relates to the technical field of medical health information. In order to solve the problems that complex risk factors of constipation are difficult to comprehensively capture, so that the feature coverage range of a prediction model is limited, group classification and risk level dynamic evaluation are not performed on users, so that an intervention scheme is high in universality, but accurate management requirements of different groups are difficult to meet. Multi-source data such as electronic medical records and physiological parameters are integrated through the multi-modal data processing module, refined classification of user groups is achieved, feature causal relationships are analyzed in combination with a knowledge graph technology, constipation prediction accuracy is improved, the intelligent prediction module dynamically adapts to a model according to group risk levels, and constipation prediction accuracy is improved. The probability distribution is output, a visual causal path report is generated, and the dynamic decision-making module adjusts an intervention scheme through doctor-patient cooperation based on a knowledge graph matching strategy set and optimizes a strategy in real time by using an effect feedback mechanism, so that personalized health management is realized.

The invention relates to a perioperative delirium risk prediction method and device and a storage medium, and belongs to the technical field of big data processing. The method comprises the steps of obtaining to-be-predicted multi-modal data; encoding the multi-modal data, and extracting global state features; fusing information of different modes through a multi-mode cross attention mechanism to generate fusion features; the fusion features are combined with coding information to be input into a recursive reasoning module, and global state features at future time points are gradually reasoned; inputting the global state features at the future time point into a perioperative period delirium risk prediction model, and generating a perioperative period delirium risk probability at the future time point; and generating a perioperative period delirium risk probability sequence in a time sequence form through recursive reasoning. According to the method, multi-dimensional perioperative delirium risk factors are covered; the capturing capability of feature interaction and association between modals is improved; the change trend of the perioperative period risk can be dynamically predicted, and the interpretability and practicability of the model are enhanced.

The invention relates to a heart failure home patient-oriented multivariable trend anomaly detection method, which comprises the following steps of: constructing an initial contour of a multivariable health trend for a patient, and introducing a trend inertia vector: updating trend inertia to form an individual trend trajectory relationship; detecting an inertia breaking point in the trend trajectory diagram; analyzing whether the variables within 12-36 hours before and after the focusing breaking point analysis generate collaborative disturbance or not; performing reprocessing through a disturbance amplification operator to form a potential early warning factor; constructing a multivariable intervention graph by using the disturbance cooperation matrix; monitoring the offset direction and strength of the causal propagation chain on each path; if the plurality of paths are subjected to direction deviation in the same period at the same time, judging that the deviation is pathological trend deviation; converting the causal offset path into a single trend risk factor, spreading a plurality of weak signals in a variable graph, evaluating systematic influence, forming a fuzzy risk scoring curved surface relationship, and outputting an individual trend risk level; the sensitivity and continuity of trend identification are improved, individual differences are adapted, and the false alarm rate is reduced.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are improved.

The invention relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

According to the physical examination item recommendation method, device and equipment based on the large language model and the computer readable storage medium, interactive physical examination questions and answers are performed with the target user according to the initial risk factors and the related question list corresponding to the candidate disease information list, the collection efficiency of the dynamic health information of the target user is improved, and the user experience is improved. And mining dynamic health information. In combination with dynamic health information, symptom information and health risk factors are further mined, and a disease information list of the target user is enriched, so that personalized physical examination item recommendation of the target user is realized. And the basic health information of the target user is combined with the dynamic health information, and the potential disease information of the target user is determined comprehensively, so that the physical examination item information more adaptive to the target user is obtained according to the comprehensive target disease information list, and the recommendation efficiency and recommendation accuracy of the physical examination item information are improved.

The invention discloses a children obesity early warning method and system based on obesity risk factor analysis, and the method comprises the steps: carrying out the feature screening of collected children multi-dimensional health related data containing parental information, birth history and lifestyle, and recognizing the important risk factors and weights of children obesity; important risk factors are supplemented through literature review; the method comprises the following steps: formulating scale questions including parental information, birth history and lifestyle based on important risk factors and weights in combination with clinical expert opinions, assigning scores to the scale questions, dividing according to a scale score result to form children obesity risk prediction levels, and providing personalized health management schemes for subjects with different risk levels. The method is low in cost, easy to implement, high in compliance and based on non-invasive characteristics, and is particularly suitable for early obesity screening and early warning of 3-6-year-old children.

The invention relates to the technical field of information management, in particular to an underground pipeline gallery risk monitoring method and device. According to the method, a heterogeneous sensing terminal network is used for sensing a target underground pipeline gallery according to a fast and slow time sequence, and a pipeline gallery monitoring anchor point set is determined; performing cross-modal interaction on a heterogeneous sensing terminal network based on the pipeline and pipe gallery monitoring anchor point set to obtain a pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set; performing anchor point risk assessment according to the pipeline and pipe gallery monitoring anchor point multi-modal fusion feature set, and obtaining a pipeline and pipe gallery monitoring anchor point risk factor set according to an assessment result; and when the pipeline and pipe gallery monitoring anchor point risk factor set is greater than or equal to a preset anchor point risk factor threshold value, triggering a risk early warning instruction. According to the method, accurate identification and dynamic early warning of the key risk points of the underground pipeline gallery are achieved through fast and slow time sequence sensing and cross-modal feature fusion processing based on the monitoring anchor points.

The invention discloses a food safety event intelligent early warning system based on multi-source data analysis, and relates to the technical field of food safety early warning, the system can enhance the adaptability and flexibility of a model through continuous self-optimization and adjustment, the system can become more accurate and efficient along with the increase of the data volume, and the early warning efficiency is improved. And problems caused by long-term dependence on fixed rules and outdated models are avoided. By fusing the data of a plurality of sensors, the system can timely discover various potential pollution sources and identify the risk of cross contamination, and risk factors possibly neglected by a traditional method are avoided. The intelligent decision support function of the system greatly reduces the burden of manual intervention, improves the timeliness and accuracy of the reaction, and avoids the delay or error caused by manual judgment. The early warning can significantly improve the response speed, reduce the occurrence probability of food safety problems, reduce the potential safety hazards in food production, and guarantee the health and safety of consumers.

The invention relates to the technical field of diabetes prediction, in particular to a method for predicting early diabetes based on health data, and the method comprises the steps: carrying out the data collection and preprocessing of an individual health system, and obtaining diabetes risk related data; performing off-line calculation on the data by using a feature correlation analysis technology, and extracting a diabetes prediction feature set; performing risk assessment on the feature set through a preset diabetes prediction model to generate a diabetes risk prediction result; key risk factors are extracted, a risk factor classification model based on a random forest is constructed, risk attribution analysis is carried out, and main risk factors are identified; then, comprehensive assessment is carried out on the main risk factors in combination with confidence propagation analysis and risk path analysis technologies, and a core risk source is positioned; the individual diabetes risk portrait is constructed according to the core risk source, the relationship among the risk factors is analyzed, a personalized early intervention scheme is formulated, and the comprehensiveness, precision and interpretability of early diabetes prediction are remarkably improved.

The invention relates to a cardiovascular disease risk assessment system and method. On the basis of fully considering the influence of dimension index information of kidney-related risk factors on the occurrence risk of cardiovascular diseases, heart and kidney metabolic indexes are integrated. And a mathematical model Fine-Gray is used for screening predictive factors by a step-by-step method. On the premise that traditional cardiovascular disease risk factors are reserved, in order to give consideration to the accuracy and easy generalization of risk assessment, prediction factors are simplified through strict calculation and derivation; the method is constructed based on a small amount of clinically ubiquitous detection index information such as age, sex, smoking or not, systolic pressure (SBP), fasting blood glucose (FBG), non-high density lipoprotein cholesterol (non-HDL-C) and estimated glomerular filtration rate (eGFR) as predictive factors. The method has the advantages of simplicity and convenience in operation, less required information and low professional requirement. Meanwhile, the risk of the outcome of various clinical cardiovascular diseases (including CVD, ASCVD and HF) can be evaluated at the same time, and the method can be suitable for various scenes such as primary medical care and clinical diagnosis and treatment.

The invention discloses a hospital pharmacy inventory intelligent management system and method, and belongs to the technical field of data management. Medicine basic information and multiple batches of inventory records are acquired; an application risk grade R is extracted based on the medicine application label; combining the remaining validity period, the stability parameter and the risk factor to construct a batch ex-warehouse priority scoring model P; generating a sorting ex-warehouse list and executing allocation and ex-warehouse operation; when it is monitored that the medicine is in time or the inventory is abnormal, dynamically adjusting the score and updating the sorting result; according to the invention, fine control of the drug delivery sequence can be realized, clinical availability of high-risk-purpose drugs is guaranteed preferentially, and inventory use efficiency and medication safety are improved at the same time.

The invention relates to the technical field of food safety data processing and intelligent early warning, and particularly discloses a safety early warning method and system based on food safety inspection data. The method comprises the following steps: acquiring multi-source food safety inspection data, performing structured preprocessing, and constructing a unified feature vector space; extracting space-time correlation characteristics to generate a dynamic risk factor sequence; inputting the sequence into an interpretable fusion model for score calculation, and generating a risk score matrix; fusing the product information to generate an attribute labeling vector, constructing a food safety knowledge graph and carrying out causal reasoning; and generating early warning information and a risk tracing path based on a reasoning result, and carrying out reverse correction and model updating on the risk factors. According to the method, multi-source data modeling, causal relationship reasoning and a model self-learning mechanism are fused, dynamic identification and closed-loop early warning of food risks are realized, and the method has high adaptability and interpretability.

The invention, which relates to the technical field of the emergency internal medicine department, discloses an intelligent assessment and early warning system for the illness state of the emergency internal medicine department, and the system carries out quantitative assessment based on pathological characteristics of multiple dimensions through a comprehensive assessment and early warning module and a blood sugar fluctuation and fundus disease analysis module. The integration of the data can form a comprehensive fundus lesion risk factor Rrisk, and the fundus lesion risk of a diabetic patient is subjected to all-around quantitative evaluation. According to the system, fundus lesion risks of patients are divided through set risk levels, and intervention suggestions of different levels are provided in combination with a comprehensive fundus lesion risk factor Rrisk. When the patient is at a high risk level, the system can trigger related early warning in time to remind medical staff to take targeted treatment or further examination, and serious consequences such as disease development to blindness and the like are avoided. The multi-dimensional comprehensive evaluation not only improves the diagnosis accuracy of the fundus lesion of the patient, but also provides powerful decision support for clinical treatment.

The invention discloses an intelligent VTE prevention and control system, and the system comprises a risk assessment module which is used for obtaining a risk index of VTE according to a risk factor; the preventive measure recommendation module is used for recommending preventive measures according to the clinical data; the VTE early warning module is used for acquiring an early warning index of the VTE according to the iconography data, the laboratory examination result and the clinical symptoms; and the treatment scheme optimization module is used for adjusting the treatment scheme according to the individual characteristics, the treatment reaction and the drug interaction, and helping a doctor to provide an auxiliary reference result for the illness state of the patient and subsequent treatment.

The invention provides a dynamic prediction algorithm for monitoring late-onset infection of a premature infant and an upgrading system. The dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system comprise the following steps: a, collecting clinical data of the premature infant, including birth weight, gestational age, 1-minute and 5-minute Apgar scores, right hand perfusion index, lower limb perfusion index and other related clinical information, and b, determining the early-onset infection of the premature infant through medical history collection and physical sign analysis. The method comprises the following steps: collecting data of 11 classification independent variables: prenatal antibiotic use conditions (existence and absence); according to the dynamic prediction algorithm for monitoring late-onset infection of the premature infant and the upgrading system, the infection risk index is effectively calculated through high-risk factors analyzed by the Lasso regression model in combination with clinical basic data of the premature infant, early warning of infection of the premature infant is provided for medical staff, and the accuracy and timeliness of infection prediction are remarkably improved. Besides, the system can automatically remind medical staff to intervene the high-risk child patient through red warning, so that the death rate caused by delayed discovery and delayed treatment is reduced, and the clinical intervention effect is improved.

The invention discloses a method and system for constructing a weak home screening evaluation and personalized intervention model based on a knowledge graph and a large model, and the method comprises the steps: data collection and arrangement: collecting multi-source data related to weak, including physical examination data, life style data, disease data and the like, cleaning, standardizing and integrating the data, and storing the data in a database; forming a structured weakness-related data set; a knowledge graph construction step of constructing a weakness knowledge graph based on the collected data, including entity recognition, relation extraction and knowledge fusion, and establishing a relation between entities by taking concepts, symptoms, risk factors, intervention measures and the like related to weakness as entities; preliminary screening of weakness: performing preliminary screening on the individuals by using an FRAIL scale, and evaluating whether the individuals are in weakness or in the early stage of weakness; asthenia risk factor assessment: further assessing the multi-modal risk factors of individuals with the preliminary screening result of asthenia or the early stage of asthenia, including lifestyle, hearing loss, environmental factors, drug management, mental health, coexistence of chronic diseases and multiple diseases, sleep quality, social isolation, malnutrition and cognitive problems; and making personalized intervention suggestions: providing the personalized intervention suggestions for the individuals according to the evaluation result in combination with intervention measures and individual characteristics in the knowledge graph.

The invention relates to the technical field of biomedical engineering, and discloses a VTE system execution method and system combined with a standardized scale, and the method comprises the steps: carrying out the standardization processing of original data, and obtaining the standardized data; constructing a risk intensity flow; carrying out integral operation to obtain an accumulated integral value of the risk factor; calculating a dynamic risk trajectory; fusing the baseline score value of the standardized scale with the dynamic risk trajectory to obtain a total risk trajectory; calculating an instantaneous risk probability, and judging whether VTE risk early warning is triggered or not according to a self-adaptive threshold value; according to the method, continuous quantitative monitoring of VTE risk factors is achieved by constructing the body position, anesthesia, hemostatic and circulation parameter four-dimensional risk intensity flow, early recognition and timely early warning of VTE risks are achieved through double mechanisms of continuous triggering and acute triggering, and the accuracy, the real-time performance and the individualized level of risk assessment are remarkably improved.

The invention discloses a breast cancer risk prediction method based on machine learning and multi-dimensional data, and belongs to the technical field of medical health information.The method comprises the steps that based on an NHANES database, diet, living habits and other information are collected, and a data set is formed; performing pretreatment; screening meaningful data features by using three feature selection methods of LASSO regression, mRMR and forward selection, and obtaining a final feature data set after intersection; dividing a training set and a test set; establishing a risk prediction model by using an SVM machine learning method, and learning the training set; performing model performance analysis on the test set to obtain a risk prediction probability of a final training model; the method has the advantages of multi-source data integration, high-precision prediction, personalized evaluation, dynamic updating and the like, risk factors of the breast cancer can be effectively mined, theoretical support is provided for prevention and treatment of the breast cancer, high-risk group screening is guided, morbidity reduction is assisted, early diagnosis and early treatment are achieved, and development of female health undertaking is promoted.

The present invention proposes an intelligent management system for chronic comorbidities in the elderly, including a comprehensive elderly assessment module, a multidisciplinary collaborative diagnosis and treatment module, a multiple medication risk prediction management module, a chronic disease intelligent follow-up management module, and a health science knowledge base module. The comprehensive elderly assessment module inputs the patient's individual chronic disease profile into a risk factor prediction model for prediction and outputs a comprehensive assessment result. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / admitting physician to coordinate multidisciplinary physicians for collaborative diagnosis and treatment. The multiple medication risk prediction management module is used to provide patients with comprehensive diagnosis, multiple medication management, and risk prediction through the collaborative diagnosis and treatment. The chronic disease intelligent follow-up management module performs follow-up tracking, health risk prediction, and review prompts for patients. The health science knowledge base module matches and pushes corresponding health education knowledge. The present invention improves the accuracy and timeliness of risk assessment, prediction, and management of chronic comorbidities in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

The invention relates to the field of medical health management, and discloses a thyroid-oriented whole-cycle health management method, which comprises the following steps of: firstly obtaining thyroid multi-source information data of a target population to form a data set, then extracting fusion features to generate a time sequence feature vector, and analyzing and calculating occurrence risk factors and scores of feature dimensions in a preset time window according to the time sequence feature vector; a comprehensive risk score is obtained through time sequence analysis, risk grades are divided, high-risk individuals are marked, and a personalized screening strategy is generated; then combining historical data of high-risk individuals, analyzing key driving paths with high risk scores, associating an external knowledge base to predict recurrence frequency, and finally dynamically adjusting and optimizing screening and intervention strategies according to the recurrence frequency; precise evaluation, grading and personalized management of individual thyroid health risks are achieved, then risk key driving paths are found out, recurrence is predicted, and the strategy is dynamically optimized to improve the disease prevention and treatment effect.

The present invention relates to the technical field of medical information management for the elderly endocrine system, and specifically relates to a method for risk assessment of elderly endocrine diseases based on big data. The present invention screens out all the highest similarity vectors of the target medical vector; obtains the basic risk factors according to the detection time distribution and similarity degree between the two; obtains the self-risk factors by combining the detection times of all medical vectors; obtains the data change risk factors according to the number of similar patients with the disease and the data difference and detection time difference between the medical vector and the first medical vector; obtains the endocrine system stability factor by using the similarity degree between medical vectors, combines them to obtain the dynamic risk factor, and further conducts a risk assessment of elderly endocrine diseases for the person to be evaluated. The present invention takes into account the physiological changes of the elderly population and the timeliness of risk factors, making the risk assessment of elderly endocrine diseases more accurate.