89 results about "Body mass index" patented technology

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 relates to the technical field of disease prediction, in particular to an intelligent risk prediction method based on a neural network, and the method comprises the steps: collecting blood glucose and body mass index data, carrying out the clustering analysis, and constructing a multi-risk group feature; a risk transfer track before attack is recognized by combining a historical index change path and time sequence analysis; and calculating an individual risk coefficient by using deep learning, and comparing the individual risk coefficient with a risk threshold to generate early warning information, thereby realizing dynamic accurate evaluation. According to the method, by collecting the blood glucose level and body weight index data and conducting standardization processing, risk group division based on health index clustering can be achieved, a group recognition system with health feature differentiation is constructed, and the structured understanding of the individual health trend is enhanced. And on the basis of the clustered group, extracting a fluctuation path of individual historical indexes by means of a time sequence mode, identifying key transfer characteristics before disease attack, and enhancing the traceability of the disease formation process.

This application discloses a rapid detection method for screening of dwarfism in children, which belongs to the field of intelligent medical technology. It includes the following steps: Step 1, accurately sampling through capillary effect, and automatically diluting the sample to ensure accurate concentration; Step 2, introducing the collected sample into a multi-channel microfluidic detection chip to achieve parallel quantitative detection of endocrine hormones; Step 3, real-time acquisition and amplification of weak biomolecule recognition signals to effectively improve the signal-to-noise ratio; Step 4, multi-dimensional analysis of the test results to achieve quantitative evaluation of growth and development, body mass index and sexual development indicators and risk prediction of endocrine diseases; Step 5, automatically integrating the analysis results and generating a standardized test report; Step 6, establishing a quality control mechanism throughout the entire detection process, including real-time monitoring of sample quality, tracking of detection process parameters, result consistency evaluation, and full traceability of detection data.

According to the clinical nutrition intelligent management system and method disclosed by the invention, nutrition status evaluation is carried out by applying a nutrition comprehensive evaluation formula, the standardization and objectivity of evaluation are greatly improved, the formula quantifies multiple key factors and gives weights, different evaluators carry out calculation based on the same information and formula, and the consistency of evaluation results is ensured; besides, the comprehensive nutrition evaluation formula comprehensively considers the body mass index, the disease-related nutrition influence coefficient, the nutrition-related index comprehensive score, the diet health degree score and other aspects adjusted by age and gender, accurately reflects the actual nutrition condition of the patient, and solves the problem of insufficient accuracy of traditional evaluation. And the nutrition plan generation module can customize nutrition plans for patients according to the nutrition comprehensive evaluation indexes in combination with a preset rule base, so that individual requirements of different patients are fully met, and a scientific, accurate and personalized solution is provided for clinical nutrition management.

The invention discloses an oxygen concentration monitor and a monitoring method thereof, relates to the technical field of oxygen concentration monitoring, and effectively improves the accuracy and reliability of the monitor through comprehensive performance parameter acquisition, calibration judgment, body mass index analysis, sampling frequency setting, environment evaluation and compensation algorithm selection. According to the method, physical condition evaluation of a monitored patient can be optimized, and more accurate monitoring data can be provided according to environmental conditions, so that clinical requirements are better met, reliable monitoring services are provided, the medical quality is further improved, the calibration precision and efficiency are improved, and the accuracy and reliability of a monitor are ensured; through precise performance adjustment and calibration judgment and personalized monitoring service, the efficiency of the monitor can be improved, and unnecessary maintenance cost and human input are reduced. Therefore, the efficiency and quality of medical services can be improved, and medical resources can be saved at the same time.

The invention discloses a heart tension index dynamic calculation and risk assessment method based on HRV, and the method comprises the steps: carrying out the filtering and denoising of an electrocardiosignal through a Butterworth filter, and obtaining a filtered electrocardiosignal; calculating interval data of adjacent wave crests; processing the interval data according to fast Fourier transform to obtain frequency domain features, and combining the interval data to obtain time domain features; performing normalization processing on the time domain and frequency domain features based on a maximum and minimum normalization method; calculating a cardiac tension index according to the normalized features and a pre-trained multiple linear regression model; matching a pre-constructed reference database according to the individual age, gender and body mass index to obtain a reference cardiac tension index; calculating a cardiovascular risk score in combination with the normalized frequency domain features and a reference cardiac tension index; and repeating the above steps regularly, smoothing through a moving average method based on continuous multiple cardiovascular risk scores, and analyzing the trend to send out cardiovascular risk early warning information.

The invention discloses an intelligent system for acquiring and analyzing sports data, and relates to the technical field of data analysis. The method comprises the following steps: acquiring heart rate data and speed data of a user, respectively constructing a heart rate data curve graph and a speed data curve graph, acquiring a climbing point and a climbing index of the user, setting analysis periods, acquiring constant indexes in different analysis periods, generating a body mass index of the user, and constructing a constant evaluation model. The method comprises the following steps: acquiring recommended exercise duration of a user by utilizing a constant evaluation model, setting a heart rate standard range, acquiring an abnormal point of the user and a speed variable amplitude of the abnormal point, constructing a variable amplitude evaluation model, acquiring a heart rate trend of the user by utilizing the variable amplitude evaluation model, generating a recommended exercise speed in real time, and feeding back the recommended exercise speed; according to the technical scheme, the aerobic exercise efficiency of the user can be improved, and the aerobic exercise quality of the user can be guaranteed.

The invention discloses a student sports performance assessment method based on weighted GG clustering. The method comprises the following steps: 1, acquiring all body test item data and corresponding evaluation grades of students of all grades, wherein the body test items comprise body weight index, vital capacity, 50-meter running, sitting-position body forward flexion, standing long jump, pull-up (male) / 1-minute sit-up (female) and 1000-meter running (male) / 800-meter running (female); 2, normalizing the acquired body measurement data of each item to [0, 1]; 3, training a weighted GG clustering model by using data obtained by normalization and corresponding scores, and accurately determining a clustering center of each physical test level; 4, classifying actually measured physical measurement parameters by using the weighted GG clustering model trained in the step 3, and obtaining an accurate physical measurement result; and 5, taking an unreasonable or inaccurate result of physical test score evaluation as a new sample to correct the model so as to improve the precision of the model. The physical test score of the student can be rapidly and accurately judged, and the method has good practical application value.

The invention relates to a postoperative hypoxemia risk prediction method and system.The postoperative hypoxemia risk prediction method comprises the steps that patient data are obtained, and the patient data comprise the body mass index, the platelet count, the fibrinogen level and the triglyceride-glucose index; the patient data are input into a pre-trained postoperative hypoxemia risk prediction model to obtain a postoperative hypoxemia risk prediction result, and the postoperative hypoxemia risk prediction result comprises the postoperative hypoxemia occurrence probability, the 95% confidence interval and the model parameters. The method has the advantages that the preoperative data, namely BMI, PLT, FIB and TyG, can be used for predicting the postoperative hypoxemia risk of the patient, so that the patient with high postoperative hypoxemia risk can be focused after the operation; preoperative data is easy to obtain and can be obtained from routine examination of a patient, and the method is simple, rapid and convenient to operate; the speed of obtaining the postoperative hypoxemia risk prediction result is high, and the accuracy rate is high.

The invention discloses a patient sign deterioration assessment processing method based on intensive care clinical data, and the method can generate a more accurate grading result based on comprehensive data through the continuous monitoring of a plurality of pieces of physiological index data and the deep analysis of historical patient data. In an analysis process of a grading result, by introducing concepts of a dynamic normal value and a dynamic standard deviation and utilizing an acute change coefficient and a long-term adaptation coefficient, a sign deterioration score can be calculated more accurately, so that changes of physiological states of patients can be captured in time, and meanwhile, by combining individual difference factors such as ages and body mass indexes of the patients, the physical sign deterioration score can be calculated more accurately. The dynamic normal value of each physiological index is calculated, so that personalized evaluation and grading are realized; the structure causal model is utilized, and the intervention strategy suitable for the current patient can be intelligently output based on the grading result by training the multiple pieces of sample intervention strategy information and the corresponding grading result, so that the scientificity and effectiveness of medical decision making are improved.

The invention discloses a method and a system for predicting preeclampsia risk of a pregnant woman based on computer implementation. The method integrates the concentration level of vitamin D (especially 25-hydroxyvitamin D) in serum of pregnant women, angiogenesis biomarkers (such as sFlt-1 / PlGF ratio), conventional clinical indexes (such as mean arterial pressure and body mass index) and personal medical history data. A machine learning algorithm (such as a random forest or a gradient lifting model) is adopted to carry out weighted analysis on the multi-dimensional data, and a dynamic and high-precision prediction model is constructed. The system comprises a data acquisition module, an intelligent prediction module and a clinical decision support module, and can provide real-time risk assessment and personalized intervention suggestions (such as a vitamin D supplement scheme) for medical staff, so that early warning and effective management of preeclampsia are realized, and the risk of pregnancy outcome caused by maternal and infant poor pregnancy is reduced.

Provided are an anti-bedsore adjusting and monitoring system and method. The system includes an air pressure determination module, an electric mattress and a visualization module. The air pressure determination module is configured to determine a body mass index according to a height and a weight of a target object, determine a target mattress air pressure according to the body mass index and send the target mattress air pressure to the electric mattress. The electric mattress is configured to perform inflation or deflation processing according to the target mattress air pressure, acquire a pressure value corresponding to each target position and send the pressure value to the visualization module, where the each target position is a position where a corresponding pressure sensor in the electric mattress contacts the target object. The visualization module is configured to determine guidance information corresponding to the pressure value according to the pressure value and display the guidance information.

A method for determining the indication for the prevention of persistent AR in the form of problem-based learning in primary school children, consisting of determining the body mass index (BMI, kg / m 2 ) within the range of +1 SDS or more, taking into account the age and sex of the child and IL 1β using the enzyme immunoassay method; with IL 1β equal to or more than 0.029 pg / ml, a child with overweight / obesity is classified as being at high risk for the development of persistent AR, requiring prevention in the form of problem-based learning.

Predicting a weight loss potential from gut microbiome data is described herein. In an example, a system accesses gut microbiome metagenomic sequence data for a subject and determines a measurement of one or more weight loss features from the gut microbiome metagenomic sequence data. The one or more weight loss features define a gut microbiome signature for weight loss response for the subject independent of a body mass index of the subject. The system determines a weight loss potential for the subject based on a comparison of the measurement of the subject to a plurality of reference measurements of the one or more weight loss features for a reference population showing variable weight loss responses. The system outputs the weight loss potential for the subject.

The invention provides a pancreatic cancer risk prediction system based on artificial intelligence. The pancreatic cancer risk prediction system comprises a data acquisition module, a risk assessment module and an output module. The data acquisition module is used for at least acquiring the body mass index, fasting blood glucose, blood glucose control condition, symptom and age of the current to-be-evaluated person. And the risk assessment module is used for receiving the data and calculating a pancreatic cancer risk probability value of the current to-be-assessed person based on a pre-constructed pancreatic cancer risk prediction artificial intelligence model. And the output module is used for outputting the pancreatic cancer risk probability value of the current to-be-assessed person and the variable contribution graph based on the SHAP. According to the technical scheme, a large prospective queue is utilized, the random forest algorithm with the optimal performance is selected to construct the accurate pancreatic cancer risk prediction model with high interpretability, accurate identification of pancreatic cancer high-risk groups is realized, and the efficiency and accuracy of early screening are improved.

The invention belongs to the field of nursing science, and particularly relates to an acute exacerbation malnutrition risk prediction model for an old chronic obstructive pulmonary disease patient and a construction method of the acute exacerbation malnutrition risk prediction model, which are used for establishing a rapid and accurate prediction model for acute exacerbation malnutrition risk screening and evaluation of the old chronic obstructive pulmonary disease patient. The invention discloses a model for predicting the risk of acute exacerbation dystrophy of an old chronic obstructive pulmonary disease patient. The risk prediction model is a multivariate Logistic regression model: P =-12.845 + 1.022 * age + 0.339 * ethnic + 0.077 * educated degree + 0.683 * gastrointestinal tract symptom + 0.035 * height-0.126 * body mass index + 1.045 * partial pressure of carbon dioxide-0.077 * chloride ion-0.017 * serum albumin-0.004 * prealbumin + 0.005 * hypersensitive C reaction protein + 0.479 * lymphocyte number. According to the acute exacerbation malnutrition risk prediction model for the patient with the chronic obstructive pulmonary disease of the aged and the construction method thereof, the prediction model for rapidly and accurately screening and evaluating the acute exacerbation malnutrition risk of the patient with the chronic obstructive pulmonary disease of the aged is established.

Techniques are disclosed for providing an avatar personalized for a specific person based on known data from a relatively large population of individuals and a relatively small data sample of the specific person. Auto-encoder neural networks are used in a novel manner to capture latent-variable representations of facial models. Once such models are developed, a very limited data sample of a specific person may be used in combination with convolutional-neural-networks or statistical filters, and driven by audio / visual input during real-time operations, to generate a realistic avatar of the specific individual's face. In some embodiments, conditional variables may be encoded (e.g. gender, age, body-mass-index, ethnicity, emotional state). In other embodiments, different portions of a face may be modeled separately and combined at run-time (e.g., face, tongue and lips). Models in accordance with this disclosure may be used to generate resolution independent output.

The invention discloses a model suitable for predicting metabolic syndrome (MetS) of people with polycystic ovarian syndrome (PCOS), a construction method and application, the model is characterized in that values are respectively assigned on the basis of indexes of a plurality of predictive variables, the predictive variables of the model are calculated, and the predictive variables of the predictive variables of the model are calculated according to the predictive variables of the predictive variables of the model, so that the predictive variables of the predictive variables of the model can be calculated according to the predictive variables of the model. The predictive variable is selected from sex hormone binding globulin, acne, maternal family menoxenia incidence history, age, apolipoprotein-B / apolipoprotein-A, fasting insulin and body mass index; and fitting the PCOS with MetS prediction model by adopting Logistic regression, and obtaining the morbidity according to the prediction model. According to the scheme, by optimizing model design, the requirement for the number of indexes is lowered, so that the application difficulty is lowered while the prediction precision is improved, and the prediction efficiency is remarkably improved.

The invention relates to the technical field of medical prognosis evaluation, in particular to a sex-specific advanced young non-small cell lung cancer prognosis model construction method, which collects clinical data of young metastatic non-small cell lung cancer patients, including sex, age, body mass index, D-2 polymer level, gene mutation state, metastasis site, tumor marker and hematology index. Data are divided into male and female queues, male specific prognostic factors (body mass index, gene mutation state and liver metastasis state) and female specific prognostic factors (D-2 polymer level and gene mutation state) are identified through Cox proportional risk regression analysis, and male and female column map prognostic models are respectively constructed based on the factors. All prognosis factors are converted into risk scores, the corresponding relation between the total score and the survival probability is established, finally, the model can output individualized survival probability prediction results of patients of different genders, and important reference is provided for clinical treatment and prognosis evaluation.

The present application relates to the technical field of glycated hemoglobin detection, and particularly relates to a glycated hemoglobin chromatographic detection method for pregnancy risk assessment, comprising the following steps: obtaining serum ferritin concentration, glycated hemoglobin concentration and C-reactive protein concentration of pregnant women; analyzing reference value weight of each pregnant woman according to distribution of serum ferritin concentration and C-reactive protein concentration of each pregnant woman and body mass index of each pregnant woman, and then obtaining data correction coefficient; obtaining serum ferritin reference value according to serum ferritin concentration, C-reactive protein concentration and glycated hemoglobin concentration of pregnant women; adjusting glycated hemoglobin concentration of a target pregnant woman according to data correction coefficient, serum ferritin reference value and serum ferritin concentration of the target pregnant woman, and then obtaining glycated hemoglobin detection result of the target pregnant woman. The present application can eliminate interference caused by iron deficiency or anemia, and make glycated hemoglobin detection result more accurate.

A portable device and method for non-invasive estimation of the level of physiological values such as blood glucose and blood cholesterol, comprising a central processing unit (1) which includes connections to: a signal emitter which emits signals via electrodes in contact with the skin, which, when processed by a bioimpedance microcontroller and said central processing unit (1), provide values such as hydration, body mass index, bone index; a digital optical sensor (4) which allows calculation of the blood oxygen value, heart rate and temperature; and enzymatic sensors (5) which determine the quantity of glucose or lactate; said central processing unit (1) calculating the physiological values on the basis of an automatic learning algorithm that has been trained with a set of clinical history data from a group of patients for whom at least values of bioimpedance, temperature, oxygen and heart rate are available.

The invention discloses a method, device and equipment for constructing a personalized digital human body model and a storage medium, and relates to the technical field of virtual digital humans, and the method comprises the steps: obtaining the age, gender, height and body weight indexes of a target object; generating a personalized three-dimensional geometric model of the target object according to the age, gender, height and body weight indexes; for six areas including the brain, the cervical vertebra, the chest, the abdomen, the pelvis and the lower limbs, calculating biomechanical scaling factors of all the areas according to the characteristic parameters of the personalized three-dimensional geometric model and the idealized physical models of all the areas; and generating a personalized digital human body model according to the biomechanical scaling factor, the material attribute and the personalized three-dimensional geometric model. According to the method, the accuracy of constructing the personalized digital human body model can be improved.

The invention provides an orthopedics department case information storage method and system, and belongs to the technical field of data processing.The method comprises the steps that patient attribute information in multiple pieces of orthopedics department case information is classified, and multiple classification results are obtained; the attribute information of the patient comprises age information, a body weight index and basic disease data; based on the classification result, labels are added to the multiple pieces of orthopedic case information; and storing the plurality of pieces of orthopedic case information added with the labels. According to the orthopedic case information storage method and system provided by the invention, the data query efficiency can be improved.

This invention provides an image processing-based radiotherapy method, belonging to the field of medical image processing and radiotherapy technology. This method acquires multi-dimensional data such as the patient's CT images, physiological state information, body mass index, and gender. After preprocessing the CT images, it utilizes a deep convolutional neural network and multimodal feature fusion technology to extract and fuse image features, physiological state features, weight features, and gender features to generate high-quality feature vectors. Furthermore, through semi-supervised learning, combined with labeled and unlabeled CT images, it adaptively adjusts the radiotherapy dosage, thereby achieving precise radiotherapy. This invention, employing the above-mentioned image processing-based radiotherapy method, can effectively improve the personalization and accuracy of radiotherapy planning, and has significant technical application value.

The invention relates to the technical field of medical artificial intelligence, and particularly discloses a method for establishing a disease prediction model of GCK-MODY in gestational diabetes population based on machine learning. The model takes five clinical characteristics as input variables: a body mass index before pregnancy, fasting blood glucose and glycosylated hemoglobin levels during GDM diagnosis, continuous multi-generation diabetes family history and a newly discovered predictive factor, namely GDM diagnosis week of pregnancy. A classification model is constructed through a support vector machine algorithm, and a SHapley additive interpretation method is adopted to provide global and local interpretation of a prediction result, so that model transparency is enhanced. The area of the model under a curve in a test is obviously superior to that of an existing screening standard. The invention also provides an electronic device, a storage medium and a prediction system comprising the model, which can assist clinicians in early recognition of GCK-MODY high-risk patients, provide decision support for targeted gene detection, avoid unnecessary enhanced hypoglycemic treatment of pregnant women, reduce the risk of maternal and infant complications, and promote precise medical development of gestational diabetes mellitus.

The invention discloses an achilles tendon rupture risk assessment system, and belongs to the technical field of data processing systems. The achilles tendon rupture risk assessment system comprises a calculation unit and a result output unit which are sequentially connected on a data main path. A computer program is stored in the calculation unit; when the computer program is executed by the processor, a pi value calculation method is operated; the pi value is calculated according to the following formula I: # imgabs0 #, wherein e is a natural constant, and X1 is a dimensionless value of the height of the subject with cm as a dimension; x2 is a dimensionless value of the content of triglyceride with mmol / L as a dimension in the blood of the subject; x3 is a dimensionless value of the age of the subject with the age as the dimension; x4 is a dimensionless value of diastolic pressure of the subject in mmHg dimension; x5 is a dimensionless value of the content of fasting blood glucose with mmol / L as a dimension in the blood of the subject; x6 is the dimensionless value of the total cholesterol content of the blood of the subject with mmol / L as the dimension; x7 is a dimensionless numerical value of the body mass index of the subject with kg / m < 2 > as a dimension. The system and the method provided by the invention are comprehensive, effective and accurate in evaluation.

The invention provides an in-vehicle passenger body mass index detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target region image containing an in-vehicle passenger, carrying out the face detection of the target region image, obtaining a face region image, inputting the face region image into a pre-trained body mass index prediction model, and predicting the body mass index of the in-vehicle passenger. Outputting a first body mass index of the passenger in the vehicle and a corresponding first confidence coefficient, carrying out human body upper body detection on the target area image, extracting upper body key points, obtaining geometric features of an upper body skeleton based on a position relation of the upper body key points, and inputting the geometric features into a pre-trained regression model to obtain a target area image; and outputting a second body mass index of the passenger in the vehicle and a corresponding second confidence coefficient, and performing weighted fusion on the first body mass index and the second body mass index according to the fusion weight coefficient to obtain a fused body mass index of the passenger in the vehicle, thereby solving the technical problems of inaccurate body mass index detection and relatively high detection cost in related technologies.

The invention discloses a traditional Chinese medicine acupoint patch for treating simple obesity. The ointment of the patch is prepared from vaseline and the following traditional Chinese medicine composition in parts by weight: 3-9 parts of fried semen brassicae, 2-6 parts of prepared euphorbia kansui, 2-9 parts of honey and bran fried rhizoma atractylodis macrocephalae, 2-6 parts of prepared radix aconiti lateralis praeparata, 2-6 parts of cinnamon, 2-6 parts of radix aucklandiae and 1-3 parts of rhizoma zingiberis. The weight ratio of the traditional Chinese medicine composition to the vaseline is 4: 3. In addition, the invention further discloses a preparation method and application of the traditional Chinese medicine acupoint patch. Through clinical verification, the traditional Chinese medicine acupoint patch can obviously reduce the body mass index and the body fat rate of a patient suffering from simple obesity, can effectively reduce the fat area, and has a more remarkable effect on abdominal subcutaneous fat. The effective rate of treating simple obesity after continuous application for 12 weeks is up to 83.9%, the fasting blood glucose level of a patient can be obviously reduced, and the traditional Chinese medicine composition is safe, reliable, free of obvious adverse reaction, convenient to continuously use, capable of prolonging the treatment cycle and consolidating the curative effect and suitable for clinical application and popularization.

The invention discloses a secondary cesarean delivery postpartum hemorrhage risk early warning model, and relates to the technical field of gynaecology and obstetrics medical treatment. The construction method of the early warning model comprises the following steps: data collection: collecting clinical data of a puerpera in secondary cesarean section, including basic and historical information, information of the gestation period, information of the delivery period of the gestation period and an outcome variable; single factor analysis: screening significant factors of an assisted reproductive technology, a pre-pregnancy body mass index, a pre-antenatal body mass index, pregnancy and birth times, previous cesarean birth times, the number of fetuses, gestational week termination, combined hypertension, combined hysteromyoma, a placenta and uterus relationship, fetal prolapse and uterus height. According to the method, based on large sample data and Logistic regression analysis, the gestational week of gestation termination is screened out, three independent risk factors of hysteromyoma and the relationship between the placenta and the uterus are combined, and model construction has statistical significance; risk scores are quantified through column diagram design, abstract risks are converted into visual values, and the clinical decision-making efficiency is improved.

The application discloses a glycolipid metabolism index prediction model for predicting cardiovascular events and a construction method thereof. The model aims to solve the problems that existing cardiovascular risk assessment tools are not accurate in describing glycolipid metabolism state and are single in prediction dimension. The construction method comprises the following steps: obtaining demographic information and clinical data of a subject; measuring the content of high-density lipoprotein subcomponent HDL2b; taking body mass index, lipoprotein a, triglyceride-glucose index and HDL2b as core evaluation parameters, taking whether a cardiovascular event occurs within five years as a target variable, and adopting a multivariate logistic regression to construct a prediction model. The model can realize rapid and accurate stratification of the risk of cardiovascular event occurrence of an individual by quantifying index scores and adding up. The application integrates multi-dimensional key indicators of glycolipid metabolism, has the advantages of high prediction performance, clear risk stratification and strong repeatability, and is suitable for early risk screening and evaluation of cardiovascular diseases.