An empirical study of using radiology reports and images to improve intensive care unit mortality prediction.

ABSTRACT:

Objectives

The predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images.

Materials and methods

In this work, we build a deep learning-based survival prediction model that utilizes multimodality data for ICU mortality prediction. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases predefined by radiologists, (3) bidirectional encoder representations from transformers-based text representations, and (4) chest X-ray image features. The model was evalua

SUBMITTER: Lin M 

PROVIDER: S-EPMC11841685 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature