Hospitalization Length of Stay Prediction using Patient Event Sequences

• URL: http://arxiv.org/abs/2303.11042v1
• Date: Mon, 20 Mar 2023 11:48:36 GMT
• Title: Hospitalization Length of Stay Prediction using Patient Event Sequences
• Authors: Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Strausholm, Tomer Sagi, Katja Hose
• Abstract summary: This paper proposes a novel approach for predicting hospital length of stay (LOS) by modeling patient information as sequences of events. We present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.
• Score: 4.204781617630707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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