  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.
