Clinical Narratives Matter

Feature-Level Fusion for Improving ICU Length-of-Stay Prediction

Abstract

The intensive care unit (ICU) provides life-saving care but often faces capacity constraints due to increasing demand, which can adversely affect patient outcomes. Accurate early prediction of ICU length of stay (LOS) is therefore essential for effective resource planning and clinical decision-making. Most machine learning (ML)–based LOS prediction models rely mainly on structured, tabular clinical variables (e.g., physiological measurements) and do not exploit unstructured clinical narratives, such as radiology reports, which contain rich contextual information relevant to patient care. In this paper, we propose an LOS prediction approach that integrates structured variables with features engineered from unstructured clinical data to enhance the effectiveness of ML models. Our experimental results demonstrate that the proposed multimodal approach improves the F1-score by up to 18.6% compared to models trained solely on structured data.