Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD.

ABSTRACT: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death in COPD patients. Machine learning (ML) approach is powerful but has a "black box" issue with an undirect interpretation of the ML technique. Herein, we conducted a multicentre, retrospective cohort study in two tertiary hospitals across China, primarily utilizing echocardiographic variables to build and validate an explainable prediction model based on a ML approach to predict the hospitalization death of AECOPD. For model explainability, we utilized a model-agnostic SHapley Additive exPlanations explainer to interpret the output of our final model. Our results showed that the light gradient boosting machine (LightGBM) model achieved the best performance among the 11 ML mod

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12521379 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature