Explainable multimodal deep learning models for variable-length sequences in critically ill patients

doi:10.1016/j.jbi.2026.105001

Explainable multimodal deep learning models for variable-length sequences in critically ill patients

2026-02-24

0

PRE

AI

Askar Safipour Afshar

DOI：10.1016/j.jbi.2026.105001

Original

Original request for help

Save

Abstract

En 中文

Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record (EHR) data. While incorporating unstructured notes improves accuracy, multimodal fusion and explainability remain an open challenge, particularly for variable-length temporal data. This study develops an explainable temporal modeling framework for multimodal EHR data that accommodates variable-length intensive care unit (ICU) trajectories and supports diverse outcome prediction tasks.

Keywords:

Deep learning

Multimodal EHR data

Explainable models

Variable-length sequences

Critical care

Explainable multimodal deep learning models for variable-length sequences in critically ill patients

Abstract

Journal

Organization