50 results about "Nomogram Chart" patented technology

The invention relates to a cerebral hemorrhage hematoma enlargement prediction method, system and device and a medium. The method comprises the following steps: acquiring baseline NCCT and CTA images of a patient; hematoma segmentation and three-dimensional reconstruction are carried out, and a key quantitative feature surface regularity index (SRI) and a density variation coefficient (DCV) are calculated; and inputting the SRI, the DCV and the clinical variables into the prediction model, and outputting the hematoma expansion risk probability. The prediction model is subjected to variable screening and strict verification, and risk visualization is realized through a column graph. The system comprises an image processing module, a feature calculation module, a prediction analysis module and a visualization module. According to the method, SRI and DCV are innovatively introduced to accurately quantify the hematoma morphology and density characteristics, and clinical variables are combined, so that the prediction accuracy and stability are remarkably improved; the efficiency is improved through AI-assisted segmentation; and the column graph enhances the model interpretability and the clinical decision support capability.

The application provides a risk prediction model for 28-day mortality of an elderly sepsis patient after being transferred into an ICU and a construction method thereof, determines independent risk factors related to death within 28 days through single factor and multi-factor logistic regression analysis, makes a nomogram, and constructs the prediction model of the patent, and the comparison result of the AUC of the ROC curve of the standard APACHE II score and the frailty index (FI-lab) or other multi-index model in recent years indicates that the prediction model proposed in the patent has better performance.

The application provides a model construction and verification method and nomogram for predicting the clinical prognosis of SFTS patients complicated with AP. By collecting the clinical data of SFTS patients, the independent risk factors related to adverse prognosis are screened out by using LASSO regression analysis, a multi-factor Logistic regression model is constructed, and the model is visualized by nomogram for predicting the adverse prognosis of SFTS patients complicated with AP. The model has high discrimination and calibration, and can provide an effective prediction tool for clinicians to optimize clinical decision-making.

The invention discloses an ACS patient prognosis prediction method, which is characterized in that ACS patient prognosis is predicted through a trained and verified ACS patient prognosis prediction model, and prediction factors of the prediction model comprise patient age, heart rate, heart function grading, urea nitrogen and glycosylated hemoglobin. The predictive factors are screened by adopting multi-factor Cox regression analysis. The prediction model comprises a column chart model, and a column chart scale comprises a scale score range of 0-100, a patient age range of 25-95, a heart rate range of 40-140, a heart function grading range of I-IV, a urea nitrogen range of 0-35, a glycosylated hemoglobin range of 4-15, a total score range of 0-240 and a survival rate range of 0.99-0.01.

Provided in the present invention is a grading method for evaluating the severity of the clinical symptoms of hypersplenism. In the present invention, a model for evaluating the severity of the clinical symptoms of hypersplenism is constructed by means of performing logistic regression analysis on PLT, WBC and RBC to establish a nomogram, converting regression coefficients into a visual scoring system, separately performing score assignment on the basis of primary and secondary factors, and finally constructing a total-score-based hypersplenism grading prediction model. The model in the present invention is used to evaluate the severity of the clinical symptoms of hypersplenism in patients. By means of correlation analysis of peripheral blood cell testing results of patients with hypersplenism, hypersplenism grades of the patients are evaluated, such that personalized treatment plans can be provided to the patients in a timely manner, thereby avoiding delays in treatment.

The invention belongs to the technical field of tumor prognosis prediction, and relates to a colorectal cancer allotropic liver metastasis prediction method and system.The method comprises the steps that colorectal adenocarcinoma cases are collected, the volume of an intra-tumor region-of-interest is delineated and automatically expanded to generate the volume of a peritumor region-of-interest, image omics characteristics are extracted through a pyradiomics packet, core omics characteristics are screened out, and the colorectal cancer allotropic liver metastasis prediction result is obtained. The method comprises the following steps: respectively constructing an intratumoral model and a peritumoral model, analyzing and integrating intratumoral and peritumoral core omics characteristics through logistic regression to form a combined radiomics model, integrating the combined radiomics model and clinical risk factors, establishing a column diagram for predicting the non-hepatic metastasis lifetime, and dividing patients into a low-risk group and a high-risk group according to a column diagram score threshold value; according to the method, LMFS prediction results of 1-5 years can be quickly output, and 0.6911 is set as a standardized risk stratification cut-off value so as to support risk stratification and personalized treatment decision of a patient.

The application provides a clinical feature-machine learning nomogram method for predicting prostate cancer risk stratification, relates to data processing, data analysis, machine learning and nomogram, and constructs a clinical feature-machine learning nomogram which is easy to use, good in interpretability and powerful in function by combining machine learning and nomogram technology, supports visualization of model prediction results, and realizes the function of constructing a clinical feature-machine learning nomogram for predicting prostate cancer risk stratification. Considering the simplicity and interpretability of the nomogram and the high efficiency and robustness of the machine learning model, the clinical feature-machine learning nomogram provided by the application can be used as an auxiliary tool for preoperative evaluation of prostate cancer risk stratification, and provides necessary information for individual diagnosis and treatment of prostate cancer patients.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The invention relates to a bromhidrosis postoperative wound infection risk prediction method and a bromhidrosis postoperative wound infection risk prediction system, aims to solve the problems that an existing clinical evaluation method depends on subjective experience and lacks a standardized tool, and performs quantitative prediction on the individual infection risk of a patient by utilizing a column graph model constructed based on multi-factor analysis. The method comprises the following steps: systematically collecting clinical index data of a patient, wherein the clinical index data comprises general data and operation-related indexes; inputting the data into a pre-constructed column graph model, and obtaining a comprehensive risk score by matching a corresponding score for each index and performing accumulation; and finally, the risk level is divided into a low-risk class and a high-risk class according to a preset threshold value, and an intuitive basis is provided for clinical decision making. According to the method, the operation process is standardized, the result output is visual, the method can be effectively integrated into the clinical working process, and a practical auxiliary tool is provided for medical staff to identify high-risk patients and implement targeted prevention and nursing measures, so that the quality and efficiency of postoperative management are expected to be improved.

The invention belongs to the technical field of medical diagnosis, and particularly relates to a visual column diagram construction method for identifying and diagnosing suppurative and tuberculous lumbar major muscle abscesses and interactive application. According to the invention, it is found that six indexes including serum uric acid (UA), thrombin time (TT), neutrophil absolute value (NEUT #), age (Age), fever (Fever) and erythrocyte sedimentation rate (ESR) are related to the type of the lumbar major muscle abscess, and a visual column diagram construction method for identifying and diagnosing suppurative and tuberculous lumbar major muscle abscess and interactive application are invented. And a first visual and quantifiable column diagram (Nomogram) prediction model for differential diagnosis of lumbar major muscle abscess is provided for the field. The technical problem that an abstract mathematical prediction model is converted into a visual graphic diagnosis tool which is intuitive in form and convenient to operate is solved, so that the visual graphic diagnosis tool can be separated from dependence on specific computing equipment, and clinical medical staff can quickly and accurately apply the visual graphic diagnosis tool on a diagnosis and treatment site conveniently.

The invention relates to the field of health behavior prediction and rehabilitation management, and discloses construction and application of a stroke patient home functional exercise compliance risk prediction model. The method comprises the following steps: collecting demographic and clinical data and scale evaluation data of a stroke patient, and carrying out missing value processing and preprocessing; identifying a compliance potential category by adopting potential profile analysis, and determining an optimal cutoff value of a total score of a scale through subject working characteristic curve analysis to form a dichotomy result; key predictive factors are screened in a training set by applying LASSO regression, and a'non-good compliance 'predictive model is established by incorporating the key predictive factors into multivariate Logistic regression; an online dynamic column graph webpage calculator is developed based on the Shiny technology, and a user is supported to input variables in real time and output a prediction probability and a confidence interval; and the discrimination degree, the calibration degree and the clinical net income are analyzed and evaluated through five-fold cross validation, Hosmer-Lemeshop test and decision curve analysis. According to the invention, rapid identification and hierarchical management of high-risk patients can be realized, and a basis is provided for follow-up visit and individualized intervention.

The application discloses a kidney function risk information processing method based on OCTA features and clinical data, and relates to the technical field of artificial intelligence and intelligent medical treatment. Quantitative features such as blood vessel density, perfusion area, retinal thickness and choroidal vascular volume are extracted from multi-scale fundus OCTA images of a subject, and total feature data sets are constructed in combination with clinical biochemical indexes, so as to estimate glomerular filtration rate as an outcome index for binary classification labeling. Elastic network regression is adopted to screen key modeling features, and various machine learning models such as logistic regression, random forest and gradient boosting are simultaneously trained on a training set. After 5-fold cross-validation optimization, the optimal model is selected by comprehensively considering the receiver operating characteristic curve, the calibration curve and the decision curve. A nomogram is constructed based on the optimal model, and individual early kidney function decline risk probability numerical values and auxiliary reference information of the subject are outputted for reference of clinical doctors.

The invention discloses a model for early prediction of acute kidney injury risk after geriatric sepsis patients are transferred into ICU based on clinical variables and immune inflammation indexes, 627 geriatric sepsis patients are included in the research, and the clinical variables and immune inflammation indexes of the geriatric sepsis patients in the ICU within 24 hours are collected; clinical variables of the first 6 ranked and immune inflammation indexes of the first 4 ranked related to acute kidney injury are screened out, prediction models are constructed through four machine learning algorithms respectively, the model prediction performance of the XGBoost algorithm is the best, the model prediction performance of the logistic regression algorithm is the second, and the model prediction performance of the XGBoost algorithm is the best. And a visual column diagram is made according to a logistic regression algorithm, and an early prediction model with good accuracy is established.

The invention relates to the technical field of crossing of medical artificial intelligence and precision medical treatment, and discloses a head and neck squamous cell carcinoma prognosis prediction method based on an immunohistochemical marker, which comprises the following steps: acquiring clinical variable data and immunohistochemical marker data of a patient, and preprocessing the clinical variable data and the immunohistochemical marker data; screening prognosis prediction factors by adopting regularization regression analysis, constructing a column graph model based on a screening result, and generating a visual prognosis prediction tool; the performance of the visual tool is verified through multiple verification indexes, and the prediction accuracy is guaranteed; on the basis of the verified tool, an online prognosis prediction calculator is developed, and individualized risk stratification and prognosis prediction of the patient are achieved. By integrating multi-dimensional clinical and molecular pathology data and optimizing the model construction and verification process, the accuracy of head and neck squamous cell carcinoma prognosis prediction is improved, and the developed online prognosis tool is convenient to operate, can directly serve clinical diagnosis and treatment decisions, and has high clinical practical value.

The invention discloses a primary metastatic breast cancer postoperative BCSS column diagram prediction model and a construction method thereof, and belongs to the technical field of biomedicine. The construction method comprises the following steps: collecting clinical pathological characteristics of a patient after a preliminary diagnosis metastatic breast cancer operation; a Cox regression model is adopted to carry out regression analysis on clinical pathological features of a patient without radiotherapy, and independent BCSS predictive variables are screened out; and determining an index for establishing a column graph prediction model from the BCSS prediction variable, and establishing the prediction model. The prediction efficiency and clinical practicability of the model are evaluated through the C index, the ROC curve, the calibration curve and the clinical decision curve, the patients are divided into low-risk groups, medium-risk groups and high-risk groups according to the total score of the column diagram, and finally the benefit conditions of each risk group receiving postoperative radiotherapy are observed. The column graph model constructed by the method has good distinction degree and calibration degree, provides a basis for a clinician to formulate an individualized postoperative radiotherapy strategy, and has important clinical significance for improving long-term survival of a patient with metastatic breast cancer initially diagnosed.

The invention discloses a kit for detecting plasma IgG glycosylation level and a detection method thereof, and relates to the technical field of biological detection.Plasma IgG is specifically captured through protein A, four biotinylation lectins are combined, sialylation, galactosylation, fucosylation and mannosylation levels of IgG can be synchronously and quantitatively detected, a standardized ELISA system is established, and the kit is used for detecting the plasma IgG glycosylation level. Operation is easy and convenient, repeatability is good, and the problem that multi-dimensional simultaneous detection cannot be achieved in the prior art is solved. The kit is low in cost, does not need complex instruments, is suitable for large-scale clinical popularization, and fills the market blank of commercial IgG glycosylation ELISA kits. Clinical samples of colorectal cancer prove that patients and healthy people can be effectively distinguished, in-vitro risk assessment can be realized by combining a column diagram model, the detection efficiency is excellent, and the method can be expanded to be applied to assessment and monitoring of various tumor and inflammation related diseases, and has high clinical value.

The application belongs to the technical field of medical decision support, and discloses a knee joint postoperative distal deep vein thrombosis prediction system; by acquiring multi-dimensional parameters such as coagulation function, metabolic function, inflammation ratio index and intra-articular injection history, the sodium hyaluronate injection history is converted into an articular lesion severity score, an inflammation-coagulation coupling characteristic vector and a metabolic disturbance correction model are innovatively constructed, and the synergistic effect among multiple systems is captured. A penalty regression model with cross-validation is used for sparse prediction factor screening, redundant variables are screened out through a regularization path shrinkage technology, and key prediction factors are reserved. Finally, an individualized thrombosis risk probability nomogram is constructed, and the prediction accuracy is verified through resampling calibration. The application realizes the multi-system integration of inflammation, coagulation, metabolism and joint degeneration factors, converts the complex statistical model into a clinically friendly tool, and optimizes the postoperative thrombosis prevention and control strategy.

This invention discloses a multi-omics device for analyzing the spatiotemporal heterogeneity of immunometabolic reprogramming in malignant pleural effusion. The device includes an integration analysis module for performing multimodal integration and consensus clustering analysis on multi-omics datasets to obtain integration analysis results. Based on the integration analysis results, the training samples of malignant pleural effusion from patients are classified into three subtypes: immunometabolic activation, immunometabolic transition, and immunometabolic inhibition. A screening and construction module is used to screen immunometabolic biomarkers from the features of the immunometabolic inhibition subtype, construct a candidate target set based on these biomarkers, and build a prediction model. An assessment and suggestion module is used to calculate treatment response scores and integrate clinical staging information to construct a nomogram, obtaining prognostic risk assessment results and personalized treatment guidance suggestions. This improves the accuracy of prognostic assessment and the reliability of treatment response prediction, enabling precise risk stratification and personalized treatment guidance for patients.

The invention provides a machine learning analysis method and system based on prostate tumor multi-dimensional disease data and electronic equipment. The method comprises the following steps: S1, acquiring original image data, original clinical data and pathological results of prostate dominant focus tissues; s2, screening to obtain target radiomics characteristics, and establishing a radiomics model according to the target radiomics characteristics and in combination with the pathological result; establishing a deep learning model according to the original image data in combination with a pathological result; screening to obtain target clinical features, and establishing a clinical prediction model according to the target clinical features and in combination with the pathological result; s3, carrying out fusion analysis on the image omics model, the deep learning model and the clinical prediction model aiming at the prediction result of the prostate tumor multi-dimensional disease data, screening to obtain a target fusion feature, and establishing a multi-modal fusion model according to the target fusion feature in combination with a pathological result; and S4, establishing a two-dimensional column diagram based on the multi-modal fusion model.

The invention relates to the technical field of medical image processing. The invention discloses a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction method and a habitat imaging-based gastric cancer neoadjuvant chemotherapy curative effect prediction system, and the method comprises the following steps: S1, obtaining pre-treatment CT and clinical pathology information, and marking the maximum tumor area and upper and lower layers; s2, dividing the marked region into habitat sub-regions with different biological characteristics through habitat imaging; and S3, performing prognosis analysis on the habitat subregion, screening independent prognosis features related to survival, and generating a habitat subregion image. And S4, constructing a joint attention model, inputting images of the marked area and the habitat subarea, predicting the curative effect and the total lifetime, and obtaining a survival score. And S5, integrating clinical pathological information and survival scores, performing prognosis analysis, constructing a column graph, and evaluating correlation. After a CT image label before treatment is obtained, habitat imaging is used for dividing subareas, related images are generated, the model is input in a combined mode, tumor heterogeneity is revealed, and the curative effect is predicted.

The present application relates to a kind of vertebral body fracture postoperative secondary fracture risk prediction model, construction method and prediction system, including the multidimensional data of collection research object;The multidimensional data of the research object meeting the requirements is standardized pre-processing;Fractured vertebral body positioning sub-network is constructed, and the output ROI of injured vertebra is input into image parameter measurement sub-network, and the output result is converted to obtain standardized image parameter set;Standardized image parameter set and clinical data are merged, and independent predictive factor is screened;Based on independent predictive factor, the risk prediction model of vertebral body fracture postoperative secondary fracture based on nomogram is constructed.The present application realizes the automatic, standardized extraction of multimodal image parameters by cascading CNN network, integrates multi-center patient baseline data, surgery-related data and postoperative nursing data, and can be used for accurately predicting the risk of secondary OVCF within 2 years after operation by feature screening and model construction;Solve the problems of poor generalization, strong subjectivity and insufficient patient compliance in the prior art.

The invention relates to a computer-implemented method (200) for training an artificial intelligence-based model (M) and to a computer-implemented method (600) for supporting an operation on a patient's eye, comprising such a model (M), which is used to calculate and / or provide refractive output data (933) on the basis of input data (931), and in particular to create nomograms. Nomograms from the prior art may either have an accuracy in need of improvement or, if the accuracy is improved, may generate implausible profiles of the nomogram. According to the invention, more accurate nomograms based on the output data (933) are made possible without implausible states. This is made possible by a cost function (915) which comprises a non-linear output value (917), representing a measure of the prediction error, of a univariate linear regression function and at least one non-linear penalty term (919). The invention further relates to a trained model (M) or a weighting matrix (923) of such a model (M), to a computer program product, to a data processing device (800), to a computer-readable medium, and to a laser therapy device.

A computer-implemented method (200) for training an artificial intelligence-based model (M) and a computer-implemented method (600) for supporting surgery on a patient's eye are provided, comprising such a model (M) which serves to calculate and / or provide refractive output data (933) based on input data (931), and in particular to generate nomograms. Prior art nomograms may either have insufficient accuracy or, if the accuracy is improved, generate implausible nomogram progressions. According to the invention, more accurate nomograms based on the output data (933) are enabled without implausible states.This is made possible by a cost function (915) comprising a nonlinear output value (917) of a univariate linear regression function representing a measure of the prediction error and at least one nonlinear penalty term (919). Furthermore, a trained model (M) or a weighting matrix (923) of such a model (M), as well as a computer program product, a data processing device (800), a computer-readable medium, and a laser therapy device are described.

The invention discloses a colorectal cancer postoperative risk prediction method, device and equipment and a storage medium, and relates to the technical field of colorectal cancer postoperative prediction, based on a 3D convolutional neural network model, CT medical image data of a target object is segmented, and a segmentation result of a body composition area and a tumor primary focus area is obtained; extracting body composition indexes and radiomics characteristics of the tumor primary focus area from the segmentation result; screening the clinical pathological risk factors and the body composition indexes to obtain screening factors related to prognosis of the target object; and integrating the screening factor and the radiomics characteristics of the tumor primary lesion area so as to comprehensively evaluate the postoperative total lifetime prediction result of the target object. According to the method, the body composition and the tumor area are automatically segmented, and the column graph is constructed in combination with the radiomics characteristics and the clinical pathological risk factors, so that individualized accurate prediction of the total postoperative lifetime of the colorectal cancer patient is realized, and the efficiency and the prediction precision are remarkably improved.

The application discloses a kind of long-term prognosis prediction system and method of liver disease patient, comprising the following steps, step S1, the clinical data of cirrhosis acute decompensation and acute-on-chronic liver failure patient are collected, including anthropometry data, vital signs, laboratory data and the albumin binding capacity obtained by specific detection;Step S2, the competitive risk model for predicting the death of cirrhosis acute decompensation and acute-on-chronic liver failure patient after 1 year of admission is constructed, the prediction model is constructed using competitive risk model, and dynamic nomogram visualization prediction model is made into convenient prediction tool;Step S3, the prediction performance of model is evaluated by receiver operating characteristic curve.The albumin binding capacity and the long-term prognosis of decompensated cirrhosis and acute-on-chronic liver failure patient establish index relationship with great clinical significance, which can further optimize clinical management and guide drug treatment.

The present application belongs to the technical field of prediction model, and particularly relates to a nomogram system for predicting prognosis of esophageal cancer by combining metabolic score, pathological information and treatment method. The present application provides a nomogram system for predicting prognosis of esophageal cancer by combining metabolic score, clinical information, pathological information and treatment method, and belongs to the technical field of prediction model. The system is simple to operate, has good discrimination, calibration ability and clinical net benefit, and can be used as an important tool for individualized prediction of prognosis of esophageal cancer patients. The nomogram system considers the nonlinear influence of metabolic syndrome components on esophageal cancer, uses RCS curve to calculate metabolic score of metabolic syndrome components affecting death risk of esophageal cancer patients, more clearly observes the estimated correlation between metabolic syndrome components and esophageal cancer mortality results, and determines any potential threshold effect. The nomogram system considers the esophageal cancer adjuvant treatment decision model of adjuvant radiotherapy and chemotherapy factors, so that the prediction performance of the system is better.

A method for constructing a predictive model for the 28-day mortality risk of patients with severe traumatic brain injury (sTBI) includes: collecting clinical data of patients undergoing sTBI surgery; screening samples that meet the inclusion and exclusion criteria and grouping them according to 28-day survival; collecting clinical indicators of the samples and calculating serum sodium variability on the 8th postoperative day; screening candidate variables using LASSO regression and simplifying variables through correlation analysis and multicollinearity diagnosis; dividing the dataset into training and test sets, and determining independent predictors through univariate and multivariate logistic regression analysis; constructing a nomogram prediction model based on the independent predictors, and validating the model performance through ROC curve, calibration curve, and decision curve analysis. The predictive model of this invention uses GCS score, oxygenation index, and 8-day serum sodium variability as core indicators, exhibiting high predictive accuracy and strong clinical operability, providing a quantitative basis for prognostic assessment and clinical intervention for sTBI patients.

The invention discloses an epidural delivery analgesic lower head dystocia risk prediction method and system. The method comprises the following steps: retrospectively collecting clinical feature data of a parturient to be parturified under epidural delivery analgesia; the method comprises the following steps: determining classification tangency points of continuous variables according to clinical medicine consensus, literature review or statistical distribution, converting the continuous variables into ordered or disordered classification variables, performing code conversion on the collected classification variables, and constructing a structured data set; carrying out preliminary screening on all feature data by using single-factor logistic regression analysis and carrying out multi-collinearity diagnosis; the screened features are incorporated into a multi-factor logistic regression model for optimization to determine key prediction variables, and a logistic regression prediction model is constructed to derive a logistic regression equation; and converting the logistic regression model into a Nomogram column graph model, and outputting a prediction result of the occurrence risk probability of the head dystocia. According to the scheme, early risk prediction of epidural delivery analgesia lower head dystocia is realized, and an earlier decision window is provided for clinic.

This invention discloses a method and apparatus for predicting the risk of urinary tract infection (UTI) in children with urinary tract stones based on nomograms. The method includes: acquiring and preprocessing clinical data of pediatric patients to obtain a raw dataset; performing univariate analysis on the training set based on clinical indicators to obtain candidate variables significantly associated with UTI; performing multivariate logistic regression analysis on the candidate variables to obtain independent predictors; constructing a nomogram model based on the independent predictors; calculating a corresponding score for each independent predictor in the nomogram model; calculating the total score of the independent predictors and obtaining the prediction probability; and performing quantitative evaluation and curve analysis on the prediction probabilities to obtain the UTI risk assessment results. This invention is the first to organically integrate four indicators: gender, mean CT value, U-LEU, and NIT, greatly improving the efficiency of clinical decision-making.

The invention discloses a neonatal asphyxia risk prediction model construction method and device, which are applied to the field of computer models for disease prediction, and are used for acquiring clinical feature data of a neonatal asphyxia group and a healthy neonatal group, including pregnant mother information, fetus information and other information; performing single-factor analysis on clinical risk factors between the two groups of data, and screening out single-factor predictive variables; and by taking the screened variables as independent variables and taking whether suffocation occurs as dependent variables, carrying out binary Logistic regression analysis by adopting a forward stepwise method, and establishing a risk prediction model. A visual column diagram is drawn according to the prediction model, and the prediction model has good distinction degree and calibration degree through evaluation. According to the method, key independent risk factors are screened from multiple factors through a scientific statistical analysis method, a prediction model beneficial to early clinical recognition of neonatal suffocation high-risk groups is constructed, a basis is provided for timely making individual intervention measures, and the method is of great significance in preventing neonatal suffocation and improving the survival rate of neonates.