A deep learning model for clinical outcome prediction using longitudinal inpatient electronic health records.

ABSTRACT:

Objectives

Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data.

Materials and methods

The TECO model was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality and was validated externally in an acute respiratory distress syndrome cohort (n = 2799) and a sepsis cohort (n = 6622) from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Model performance was evaluated based on the area under the rec

SUBMITTER: Rong R 

PROVIDER: S-EPMC11984207 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature