Hospitalization Length of Stay Prediction using Patient Event Sequences

Autor:	Hansen, Emil Riis, Nielsen, Thomas Dyhre, Mulvad, Thomas, Strausholm, Mads Nibe, Sagi, Tomer, Hose, Katja
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence 68T07 I.2.7 J.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.11042 Zobrazit plný text záznamu View this record from Arxiv