首页|Affiliated Hospital of North Sichuan Medical College Reports Findings in Chronic Obstructive Pulmonary Disease (Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive ...)
Affiliated Hospital of North Sichuan Medical College Reports Findings in Chronic Obstructive Pulmonary Disease (Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Lung Diseases and Cond itions - Chronic Obstructive Pulmonary Disease is the subject of a report. Accor ding to news reporting originating from Nanchong, People's Republic of China, by NewsRx correspondents, research stated, "Frailty is a medical syndrome caused b y multiple factors, characterized by decreased strength, endurance, and diminish ed physiological function, resulting in increased susceptibility to dependence a nd/or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens."Our news editors obtained a quote from the research from the Affiliated Hospital of North Sichuan Medical College, "Therefore, the aim of this study was to deve lop a reliable and accurate vulnerability risk prediction model for frailty in p atients with COPD in order to improve the identification and prediction of patie nt frailty. The specific objectives of this study were to determine the prevalen ce of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indica tors, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive sy nthetic sampling technique was used for unbalanced data. Three methods, ridge re gressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) technique s including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbor s were used to analyze and determine the optimal model. For customized risk asse ssment, an online predictive risk modeling website was created, along with Shapl ey additive explanation (SHAP) interpretations. Depression, smoking, gender, soc ial activities, dyslipidemia, asthma, and residence type (urban rural) were pred ictors for the development of frailty in patients with COPD. In the test set, th e XGBoost model had an area under the curve of 0.942 (95% confiden ce interval: 0.925-0.959), an accuracy of 0.915, a sensitivity of 0.873, and a s pecificity of 0.911, indicating that it was the best model."
NanchongPeople's Republic of ChinaAs iaChronic Ob-structive Pulmonary DiseaseCyborgsEmerging TechnologiesHealt h and MedicineLung Diseases and ConditionsMachine LearningPulmonary Diseas eRisk and Prevention
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