46 results about "Single factor analysis" patented technology

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a postoperative sub-delirium syndrome risk prediction system for a cardiac surgery patient, and relates to the technical field of medical treatment, and the system comprises a clinical data collection module which is used for collecting multi-dimensional clinical data of the cardiac surgery patient in a perioperative period under extracorporeal circulation, the data comprises patient baseline information, preoperative neurocognitive function evaluation indexes, intraoperative physiological parameters and postoperative early-stage vital signs; and the risk identification module is used for utilizing a single-factor statistical analysis mechanism. According to the postoperative sub-delirium syndrome risk prediction system for the cardiac surgery patient, accurate prediction of the sub-delirium syndrome risk is achieved by collecting multi-dimensional clinical data of the patient in the perioperative period, such as baseline information, preoperative neurocognitive function, intraoperative physiological parameters and postoperative early-stage vital signs; the independent risk factors are scientifically screened and quantified by using single factor analysis and a multivariate Logistic regression model, and the problem of incomplete data dimensions in traditional evaluation is effectively solved.

The embodiment of the invention provides a myocardial infarction risk prediction model training method and related equipment, and belongs to the technical field of machine learning. The myocardial infarction risk prediction model training method comprises the steps of obtaining initial training data, performing multiple times of characteristic variable screening on the initial training data through a single factor analysis method and an LASSO regression method to obtain training case data, and inputting the training case data into a BP neural network model for nonlinear data processing to obtain the prediction probability of myocardial infarction occurrence. And performing parameter optimization on the BP neural network model according to the prediction probability and the real occurrence probability to obtain a myocardial infarction risk prediction model. According to the embodiment of the invention, nonlinear processing can be carried out on high-dimensional or multivariable training case data through the BP neural network model, and the processing capability of complex data and the accuracy and stability of myocardial infarction risk prediction are improved.

The invention belongs to the technical field of oil and gas field development engineering, and particularly relates to a construction method of an emulsion generation kinetic model. The method is based on a micro-fluidic experiment, comprehensively considers seven factors of pore throat radius, pore throat form, pore throat coordination number, displacement environment temperature, surfactant concentration, injection speed and crude oil viscosity, and determines an undetermined coefficient of a function by utilizing a least square method through single factor analysis and orthogonal experiment scheme design; therefore, the functional relationship among the pore throat radius, the pore throat form, the pore throat coordination number, the displacement environment temperature, the surfactant concentration, the injection speed, the crude oil viscosity and the emulsion generation rate and the generation radius is defined, and an emulsion generation kinetic model is constructed, so that an emulsion generation theoretical system under the multi-factor coupling effect is perfected; and reference is provided for other similar chemical flooding.

The invention discloses a lithofacies paleogeography single factor analysis method, medium, equipment and product, and relates to the technical field of lithofacies paleogeography analys.The method comprises the steps that lithofacies description, well position coordinates and stratigraphic stratification data in logging interpretation data are obtained; performing binary classification on lithofacies types according to whether the lithofacies description accords with the definition of the target lithofacies; based on the stratigraphic data and the binary lithofacies type, three single factor indexes of each well are calculated, and the three single factor indexes are the total stratum thickness, the target lithofacies cumulative thickness and the rock-to-ground ratio; according to the well position coordinate data and the three single-factor indexes, well points in the whole area are paired in pairs, experimental variation function values under different lag distances are calculated, and experimental variation scatter diagrams of the three single-factor indexes are drawn respectively; kriging gridding interpolation is carried out on the scatter points, contour maps of the three single-factor indexes are drawn, and lithofacies paleogeography analysis is carried out by using the three contour maps. The lithofacies paleogeography single factor analysis is realized based on the logging interpretation data.

The application discloses a kind of long approach in paste flow slope angle prediction method, belong to filling technical field, solve the prediction of filling slope angle in prior art is based on experience formula or static homogeneous model, unable to consider the influence of waste rock dynamic settlement on slurry flow resistance and slope form Problem, the method includes based on paste raw material basic parameter construction paste similarity model, based on range analysis combined with interaction analysis influencing factor on the influence degree of flow slope angle, convert flow slope angle calculation model into slope angle satisfaction function, obtain slope angle optimal filling parameter;The present application can comprehensively study the influence of multiple influencing factors and their interaction on paste flow slope angle through the pre-design and implementation of orthogonal experiment scheme, avoids the one-sidedness that single factor analysis may bring, makes the influence law obtained more comprehensive and in-depth, and is different from the homogeneous flow model or simplified discrete element model that the existing numerical simulation method is mostly used.

The present application provides a kind of source on large and medium-sized oil and gas field target evaluation method based on geologic threshold research, which includes: step 1: typical oil and gas reservoir dissection is carried out;Step 2: the parameter optimization and its characterization of main controlling geologic element of oil and gas accumulation are carried out;Step 3: the evolution analysis of key elements of basin simulation is carried out;Step 4: the correlation fitting of reserve scale and geologic single element of accumulation period is carried out;Step 5: the geologic element threshold condition of source on large and medium-sized oil and gas field formation is analyzed by multiple elements.The source on large and medium-sized oil and gas field target evaluation method based on geologic threshold research establishes the correlation of key single element and oil and gas reserve scale;Based on the single factor analysis result, through the superposition of multiple factors one by one, the relationship between source on large and medium-sized oil and gas reservoir and each geologic condition is qualitatively and quantitatively analyzed, and the intuitive evaluation of geologic threshold condition is realized.

The invention provides a quantitative evaluation method for the bolt connection precision retentivity of a multi-axis numerical control machine tool based on digital twinning, and the method integrates ANSYS multi-physics field simulation, Unity 3D virtual-real interaction and machine learning technologies by constructing a real-time interaction closed loop of a physical entity and a virtual model. The method comprises the following specific steps: establishing an error transfer model to quantify the influence of static offset, vibration and creep; carrying out pre-tightening force simulation and creep-vibration coupling analysis by utilizing ANSYS; by expanding Kalman filtering calibration parameters, virtual and physical entity errors are ensured; parameter adjustment and visual interaction are realized by means of Unity 3D; and finally, calculating a precision retention index, and evaluating a precision state according to a third-level judgment standard. According to the method, the limitation of single factor analysis is broken through, multi-physical field dynamic coupling capture is realized, and traditional qualitative evaluation is upgraded into a data-driven quantitative system.

The invention is suitable for the technical field of safety protection equipment, and provides a trumpet-shaped bionic cobweb helmet filling structure design method, which comprises the following steps of: starting from the characteristics of a cobweb and a honeycomb structure, extracting cobweb radial lines, spiral lines and honeycomb hexagonal elements through similarity analysis, according to the bionics principle, deep analysis is carried out from structure, function and boundary similarity, cobweb features are subjected to three-dimensional processing, a winding mode is changed, the cobweb features are fused with honeycomb features to establish a three-dimensional preliminary trumpet-shaped bionic cobweb structure, element and size ranges are determined, structural parameters are optimized through a single-factor analysis method and a response curved surface method, and energy absorption performance is improved. According to the trumpet-shaped bionic cobweb helmet filling structure, the protective performance and the structural stability of the helmet can be remarkably improved.

The invention belongs to the technical field of road safety situation awareness, and provides a rural road network safety situation awareness method. The method comprises the steps of traffic state data collection, road condition data collection, meteorological data collection, original data base map construction, basic data base map construction, single factor analysis, analytic hierarchy process and fuzzy evaluation processing, overall road network safety situation index calculation, virtual optimization, key road section detection and fragile road section recognition. Through analytic hierarchy process and fuzzy evaluation processing, overall road network safety situation index calculation, virtual optimization, key road section detection and fragile road section identification, low-cost dynamic safety situation awareness and management and control of the rural road network are realized, and the road service level and the road network operation capability are improved.

The invention provides an MSWI full-process model hyper-parameter optimization method based on differential evolution. The method comprises the following steps: constructing an in-furnace combustion process model and a flue gas treatment process model; performing parameter analysis on the in-furnace combustion process model and the flue gas treatment process model through a single-factor analysis method, and determining optimization parameters and a value range of the optimization parameters; constructing a fitness function according to the sum of evaluation performance indexes of the in-furnace combustion process model and the flue gas treatment process model; based on the fitness function, optimizing the optimization parameter through a differential evolution algorithm to obtain a global optimal hyper-parameter; and performing parameter updating on the in-furnace combustion process model and the flue gas treatment process model through the global optimal hyper-parameters to generate an MSWI full-process model. According to the method, the hyper-parameters of the eight parallel models are automatically and synchronously optimized through the differential evolution algorithm, and the prediction precision and generalization ability of the models are improved.

The invention discloses a clinical prediction model construction method and system for treating oligometastatic non-small cell lung cancer through radioactive particle implantation, and relates to the technical field of medical model construction, and the method comprises the steps: obtaining patient data, taking survival time as a main endpoint event, employing a Cox regression model to carry out single-factor analysis on factors affecting patient prognosis, and obtaining a single-factor analysis result; determining the relationship between each clinical feature and survival time, and incorporating the important factors influencing the prognosis into multi-factor Cox regression analysis to determine independent risk factors influencing the survival prognosis of the patient; generating a first clinical prediction model according to each independent risk factor nano-construction; cD3 + AC, CD4 + AC, CD8 + AC, combined treatment, complete treatment and the number of metastatic lesions are taken as variables together to be included in construction of a clinical prediction model, and a second clinical prediction model is manufactured and generated. Independent risk factors influencing the prognosis of a radioactive particle implantation treatment few-metastatic NSCLC patient are confirmed, the clinical prediction model is established, the treatment prognosis can be accurately predicted, and the treatment cost is reduced. And screening of dominant benefit people is facilitated.

The invention discloses an amphibious vehicle engine compartment heat dissipation shutter fin multi-objective optimization method, which comprises the following steps: establishing a fin physical model, selecting three geometric parameters as influence factors, and determining temperature, pressure loss and comprehensive performance factors as optimization objectives; unreasonable intervals are screened out through CFD single-factor analysis in combination with different working conditions of the amphibious vehicle, and the effective value range of each factor is obtained; 17 groups of response surface test schemes are generated by adopting a three-factor three-level Box-Behnken design; constructing a response surface model based on the scheme, and obtaining an optimization target, a factor interaction effect and a second-order regression equation through CFD simulation; verifying a model prediction value and an actual value; and determining an optimal model and design parameters. According to the method, an efficient closed loop is constructed through RSM and CFD, multi-objective optimization is achieved under key working conditions, heat dissipation and aerodynamic loss are considered, a comprehensive performance index and constraint system is established, and design efficiency and engineering suitability are improved.

The invention discloses a method for predicting a paste flow slope angle in a long and narrow drift, belongs to the technical field of filling, and solves the problem that the prediction of a filling slope angle in the prior art is mostly based on an empirical formula or a static homogeneous model, and the influence of dynamic settlement of waste rocks on slurry flow resistance and a slope shape is not considered. The method comprises the steps that a paste similarity model is constructed based on basic parameters of paste raw materials, the influence degree of influence factors on a flowing slope angle is analyzed based on range analysis in combination with interaction, a flowing slope angle calculation model is converted into a slope angle satisfaction function, and optimal filling parameters of the slope angle are obtained; through pre-design and implementation of an orthogonal experiment scheme, the influence of multiple influence factors and interaction of the influence factors on the paste flow slope angle can be comprehensively researched, the one-sidedness possibly brought by single factor analysis is avoided, the obtained influence rule is more comprehensive and deeper, and the analysis efficiency is improved. The method is different from a homogeneous flow model or a simplified discrete element model which is mostly adopted by an existing numerical simulation method.

The present invention discloses a method for semi-quantitatively distinguishing the segregation of fractures and caves based on karst transformation. The key point of its technical solution is to take the internal structure of ancient karst fractures and caves as the research target. It is the first to semi-quantitatively distinguish the development degree of fracture cutting transformation, filling transformation, and collapse transformation based on karst transformation. It comprehensively analyzes the influence of the three types of karst transformation on fractures and caves, compares the results of karst transformation, and comprehensively establishes two types of models: fracture cutting and filling can lead to a separation model within the system, and fracture cutting and collapse lead to a blocking model within the system. The present invention solves the previous problems of disconnection between geophysical engraving and karst geological analysis, and the high accuracy of the analysis of the internal structure of fractures and caves by single research on filling and collapse. It effectively solves the problems of insufficient geophysical interpretation accuracy and inaccurate single factor analysis, and improves the accuracy of fracture and cave internal analysis.

The application discloses a kind of risk factors prediction model of occurrence of initial treatment bacterium positive pulmonary tuberculosis lung cavity, belong to medical data processing technical field, the clinical data of initial treatment bacterium positive pulmonary tuberculosis patient is collected in the application, using single factor analysis, Lasso regression analysis and multivariate Logistic regression analysis, male, diabetes, smoking, white blood cell count and platelet / hemoglobin ratio are screened as the independent risk factors of initial treatment bacterium positive pulmonary tuberculosis causing lung cavity, and based on this, nomogram prediction model is constructed.H-L goodness-of-fit test, receiver operating characteristic curve and decision curve analysis are verified, the model has higher prediction efficiency and clinical practical value.The application can help clinical advance to predict and intervene the cavity caused by high-risk population of pulmonary tuberculosis, provide new ideas for the diagnosis and treatment of bacterium positive pulmonary tuberculosis causing cavity.

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 history landscape element analysis and protection evaluation system based on image recognition and relates to the technical field of landscape analysis, solves the technical problems that only single or limited dimension data is used to make protection decisions, targeted analysis on the characteristics of different types of landscape areas is lacked, and it is difficult to accurately warn the landscape areas needing protection, and through setting dynamic buffer areas around the river history landscape area and the city history landscape area, the data of a certain area around the history landscape area can be monitored and analyzed, and early warning can be facilitated before the influence reaches the landscape area. Through the combination of the hydrological risk of the dynamic buffer area and the landscape area, the urban heat island influence index evaluation, the climate influence intensity and the analysis result of the second level management node, the key information influencing the protection of the history landscape can be comprehensively integrated, the one-sidedness of single factor analysis is avoided, and the protection decision is more scientific and reasonable.

The invention discloses a method for improving the reliability of a wheel spoke and rim assembly, and relates to the technical field of wheels. The method comprises the steps that control factors and noise factors of the wheel spoke and rim assembly are collected; performing primary screening on the control factors and the noise factors by using single factor analysis to remove redundant factors; designing an orthogonal simulation scheme based on the control factor to identify a main effect and an interaction effect of the control factor; optimizing the simulation analysis process of the control factor and the noise factor by using a Taguchi method based on the main effect and the interaction effect of the control factor so as to reformulate an orthogonal simulation scheme; and constructing a target function and analyzing an optimal factor group in combination with an orthogonal simulation scheme. According to the method, the reliability, the cost and the light weight of the wheel assembly are comprehensively optimized, and the consumption of computing resources is reduced.

The invention provides a method for predicting hardness of a Ti6Al7Nb surface Cu-HAp coating under a jet electrodeposition process, which comprises the following steps: preparing an electrolyte, pretreating a Ti6Al7Nb titanium substrate, pouring the electrolyte into a liquid storage tank, carrying out a jet electrodeposition coating test by taking a Ti6Al7Nb titanium plate as a cathode and a copper rod as an anode to obtain a Cu-HAp composite coating, and obtaining Vickers hardness of the surface of the coating through a hardness measurement test. According to a single-factor analysis method, the influence degree of jet electro-deposition process parameters on the hardness is analyzed, data normalization processing is performed, the most significant factors influencing the coating surface hardness are researched according to a grey relational degree method, and a prediction model of the hardness related to the jet electro-deposition parameters is established based on a multiple regression algorithm.

The invention discloses a radio frequency relay optimization design method fusing big language model priori knowledge and Kriging-IAGA. The method comprises the following steps: firstly, screening out key structure parameters and material parameters which influence insertion loss through HFSS simulation and single factor analysis; then constructing a sample library by using a method of combining single factor analysis with an orthogonal experiment, and establishing a high-precision Kriging agent model; further introducing an improved IAGA to carry out multi-parameter collaborative global optimization, and adopting an HFSS to regularly check and correct the proxy model; lLM is further introduced to serve as a process constraint device, an electromagnetic performance-process implementation dual evaluation system is constructed, semantic scoring is conducted on manufacturability of an optimal solution through the LLM, and a fitness function is corrected. According to the method, the problems of long simulation period, difficulty in processing nonlinear coupling in an orthogonal test and the like in the traditional relay optimization design are solved, the insertion loss of a high frequency band is effectively reduced, and the consistency and radio frequency performance of a product are improved.

The application discloses a prediction model for diagnosing infectious mononucleosis in children and a construction method thereof. The application adopts single factor analysis and logistic stepwise regression to screen independent influencing factors by using confirmed IM inpatient cases and suspected IM but EBV-DNA negative child cases, and adopts multiple factor regression to screen six independent influencing factors of age, ALB, GLB, PLT, LYMPH# and LYMPH% to construct a diagnosis model. Researches show that in the confirmed diagnosis scene, the multi-index combined model with blood routine and liver function has excellent diagnosis efficiency (AUC=0.986), realizes the ideal balance of high sensitivity and high specificity, provides a hierarchical tool for the precise diagnosis of IM in different clinical scenes, and is suitable for the precise diagnosis of inpatient children.

The application relates to a method for predicting the risk of postoperative complications of an acute abdomen patient, comprising the following steps: collecting clinical data of the acute abdomen patient after receiving surgical treatment, and preprocessing the clinical data; screening relevant risk factor variables through single factor analysis and multi-factor analysis, and constructing a characteristic variable set; constructing a postoperative complication risk prediction model based on the characteristic variables; the prediction model is based on an XGBoost base model, a moderate greedy algorithm is used for step-by-step parameter optimization, a plurality of optimal parameter subsets are selected to construct a plurality of XGBoost sub-models, and the MGA-XGBoost combined prediction model is combined through a weighted integration mode; the MGA-XGBoost combined prediction model is used for predicting the risk probability of postoperative complications of the patient, and important risk factors in the top list are prompted. The scheme provided by the application can accurately capture the nonlinear and high-dimensional risk correlation in the clinical data of the acute abdomen patient, improve the prediction accuracy of the risk of postoperative complications, and improve the clinical applicability.

The invention relates to a mild cognitive impairment reversal prediction method and system based on a multi-modal user portrait, and the method comprises the steps: obtaining multiple types of data of a mild cognitive impairment patient through multi-source data collection, and constructing the multi-modal user portrait; carrying out standardized preprocessing on the data, and carrying out probabilistic clustering by adopting a Gaussian mixture model to divide patient subtypes; screening predictive factors through single factor analysis and LASSO regression, and performing oversampling on the training set by adopting an SMOTE-NC algorithm to balance data distribution; a prediction model is constructed based on XGBoost, and model hyper-parameters are optimized through grid search and cross validation; after the construction and training of the prediction model are completed, the prediction model is used for carrying out reverse prediction on newly-collected multi-modal user portrait data of the mild cognitive impairment patient; meanwhile, the contribution degree of each feature in the prediction model to a prediction result is analyzed based on the SHAP value, so that the interpretability of the model is enhanced. According to the method and the system, prediction errors can be reduced, and the accuracy of predicting mild cognitive impairment reversal is improved.

The invention relates to a method and system for predicting the anxiety mood prevalence rate of a diabetic child patient, and belongs to the field of artificial intelligence prediction. According to the method, multi-dimensional clinical data such as demographic data, treatment modes, management behaviors and metabolic indexes of diabetic children are collected, evaluation results of a children anxiety mood disorder screening table (SCARED) are combined, single-factor analysis, correlation analysis and multi-factor Logistic regression analysis are carried out, and an anxiety mood prediction model is constructed. The model takes age, an insulin treatment mode and glycosylated hemoglobin as core variables, and can accurately predict the anxiety risk of a child patient. The system comprises a data acquisition module, a psychological assessment module, an analysis modeling module, a risk prediction module and a report generation module, and realizes automatic data integration, model construction and individual intervention suggestion output. Clinical verification shows that the model prediction efficiency is excellent, and the method has good clinical application value and is beneficial to early recognition and intervention of anxiety mood of diabetic children and improvement of physical and psychological health and disease management quality of the diabetic children.

The invention discloses a lung adenocarcinoma prognosis prediction method based on machine learning. RNA transcription data and clinical information of a lung adenocarcinoma patient are collected from a database, and a data set is constructed; performing gene expression data preprocessing and difference analysis to obtain differential expression genes; sequencing the risk coefficients of the differential genes by using single-factor COX analysis; carrying out survival related feature recognition on the screened genes by virtue of eight machine learning algorithms related to survival analysis to obtain related genes with prognosis values; a machine learning combinatorial algorithm is utilized to train multiple prognosis models, C-index average values of the prognosis models are calculated and compared, and an optimal StepCox-RSF algorithm is selected to carry out patient layering and prognosis prediction model construction according to feature genes; according to the risk score of the patient, predicting the total survival rate and the risk grouping condition of the patient; according to the method, the reflecting capacity of the model to the immune microenvironment is remarkably improved, the prediction precision is optimized, and a scientific basis is provided for personalized treatment of lung adenocarcinoma patients.

The present application relates to the technical field of medical imageomics, and particularly relates to a pancreatic cancer early recurrence prediction method and system based on an interpretable machine model, comprising the following steps: extracting intratumoral and peritumoral imageomics features in CT images; performing single factor analysis and multivariate logistic regression analysis on body composition parameters and clinical pathological data to obtain clinical features; constructing six groups of classifier models based on the intratumoral and peritumoral imageomics features through six machine learning algorithms, and obtaining an imageomics model according to model performance comparison; constructing a clinical-imageomics combined model by combining the clinical features and the imageomics model, and performing an interpretable SHAP analysis on the clinical-imageomics combined model. The present application combines intratumoral and peritumoral CT imageomics features with body composition parameters, constructs a machine learning model for predicting the early recurrence risk of PDAC after resection, and incorporates the interpretable SHAP analysis to enhance the transparency of the machine learning model decision-making process.

A method for constructing a frailty risk prediction model for elderly hospitalized patients and a prediction system relate to the field of medical health technology. The method selects a plurality of factors influencing frailty in elderly hospitalized patients as candidate factors, first uses a univariate analysis method to screen out statistically significant candidate factors as initial screening factors; then constructs a multivariate logistic regression model as a frailty risk prediction model, using whether the elderly hospitalized patient experiences frailty as the dependent variable and the screened initial screening factors as independent variables. The frailty risk prediction model is used to screen out statistically significant initial screening factors as frailty factors, and the screened frailty factors are set as input factors of the model; the frailty risk prediction model is used to analyze the data of each frailty factor of the subject, and outputs a prediction result of whether the subject will experience frailty based on the analysis results. The method and system of the present invention are used to predict frailty risk in elderly hospitalized patients.

The invention discloses a cervical vertebra postoperative settlement risk assessment method, device and equipment and a storage medium, and the method comprises the steps: obtaining clinical and image data sets of a settlement group and a non-settlement group of an interbody fusion cage; carrying out single-factor analysis on the data set by utilizing a statistical difference analysis method, and screening out characteristics related to settlement; then based on the settlement characteristics, independent risk factors are identified through a multi-factor regression model; constructing a settlement risk prediction model according to the independent risk factors; and verifying the performance of the model through the area under the curve and the calibration index, optimizing the parameters of the model, and outputting a settlement risk probability value by using the optimized model. Through systematic statistical analysis and model verification, quantitative evaluation of the settlement risk of the fusion cage after the cervical vertebra operation is realized, and an objective basis is provided for clinical intervention.