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

ABSTRACT:

Objective

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

TECO 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 ARDS cohort (n=2799) and a sepsis cohort (n=6622) from the Medical Information Mart for Intensive Care (MIMIC)-IV. Model performance was evaluated based on area under the receiver operating characteristic (AUC) and compared with Epic Deteri

SUBMITTER: Rong R 

PROVIDER: S-EPMC11838940 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature