Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel approach for predicting LOS by modeling patient information as sequences of events. Specifically, we present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. We performed empirical experiments on a cohort of more than 45k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.
@article{arxiv.2303.11042,
title = {Hospitalization Length of Stay Prediction using Patient Event Sequences},
author = {Emil Riis Hansen and Thomas Dyhre Nielsen and Thomas Mulvad and Mads Nibe Strausholm and Tomer Sagi and Katja Hose},
journal= {arXiv preprint arXiv:2303.11042},
year = {2023}
}