首页|Department of Geriatrics Reports Findings in Cirrhosis (Development and validati on of an explainable machine learning model for predicting multidimensional frai lty in hospitalized patients with cirrhosis)
Department of Geriatrics Reports Findings in Cirrhosis (Development and validati on of an explainable machine learning model for predicting multidimensional frai lty in hospitalized patients with cirrhosis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Cirrhosis is the subje ct of a report. According to news reporting originating from Tianjin, People’s R epublic of China, by NewsRx correspondents, research stated, “We sought to devel op and validate a machine learning (ML) model for predicting multidimensional fr ailty based on clinical and laboratory data. Moreover, an explainable ML model u tilizing SHapley Additive exPlanations (SHAP) was constructed.” Our news editors obtained a quote from the research from the Department of Geria trics, “This study enrolled 622 patients hospitalized due to decompensating epis odes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from othe r tertiary hospitals. The frail phenotype was defined according to a self-report ed questionnaire (Frailty Index). The area under the receiver operating characte ristics curve was adopted to compare the performance of five ML models. The impo rtance of the features and interpretation of the ML models were determined using the SHAP method. The proportions of cirrhotic patients with nonfrail and frail phenotypes in combined training and test sets were 87.8% and 12.2% , respectively, while they were 88.5 % and 11.5% in t he external validation dataset. Five ML algorithms were used, and the random for est (RF) model exhibited substantially predictive performance. Regarding the ext ernal validation, the RF algorithm outperformed other ML models. Moreover, the S HAP method demonstrated that neutrophil-tolymphocyte ratio, age, lymphocyte-to- monocyte ratio, ascites, and albumin served as the most important predictors for frailty. At the patient level, the SHAP force plot and decision plot exhibited a clinically meaningful explanation of the RF algorithm. We constructed an ML mo del (RF) providing accurate prediction of frail phenotype in decompensated cirrh osis.”
TianjinPeople’s Republic of ChinaAsi aCirrhosisCyborgsEmerging TechnologiesFibrosisHealth and MedicineMac hine Learning
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