Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.

ABSTRACT: This study investigated and validated all-cause in-hospital death prediction models for hospitalized pneumonia patients based on large-scale clinical data, including diagnoses, medication prescriptions, and laboratory test codes. Feature selection was performed using both large-scale feature learning with a Common Data Model (CDM) and specific pneumonia-related risk factors. A stacked ensemble mixed machine-learning model was compared with traditional machine-learning models. Accuracy, F1-score, the Area Under Precision Recall Curve (AUPRC) and the Area Under the Receiver Operating Characteristic (AUROC) were used for performance evaluation. For large-scale feature learning using a CDM, the ensemble model (LASSO LR + GBM + RF) achieved the highest performance. For the 365-day lookback, the

SUBMITTER: Lee JH 

PROVIDER: S-EPMC11985930 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature