Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.

ABSTRACT: We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This study enrolled 622 patients hospitalized due to decompensating episodes at a tertiary hospital. The cohort data were randomly divided into training and test sets. External validation was carried out using 131 patients from other tertiary hospitals. The frail phenotype was defined according to a self-reported questionnaire (Frailty Index). The area under the receiver operating characteristics curve was adopted to compare the performance of five ML models. The importance of the features and interpretation of the ML models were determined using the SH

SUBMITTER: Yang F 

PROVIDER: S-EPMC11446601 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature