Development and multi-database validation of interpretable machine learning models for predicting In-Hospital mortality in pneumonia patients: A comprehensive analysis across four healthcare systems.

ABSTRACT:

Background

Existing machine learning studies for pneumonia mortality prediction are limited by small sample sizes, single-center designs, and lack of comprehensive external validation across diverse healthcare systems. No previous study has systematically validated machine learning models across multiple large-scale databases for pneumonia mortality prediction.

Methods

This retrospective multicenter study utilized four large-scale databases to develop and validate machine learning models for predicting in-hospital mortality in pneumonia patients. MIMIC-IV served as the primary training dataset (9,410 patients), with external validation on MIMIC-III (2,487 patients), eICU (13,541 patients), and an in-house multicenter prospective cohort from fudan university (345 patients). F

SUBMITTER: Chen J 

PROVIDER: S-EPMC12486837 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature