Hospitalization Length of Stay Prediction using Patient Event Sequences
This addresses resource allocation and decision-making in healthcare organizations, but it is incremental as it applies a known transformer architecture to a specific medical prediction task.
The paper tackled predicting hospital length of stay (LOS) by modeling patient data as event sequences, proposing a transformer-based model called Medic-BERT (M-BERT) that achieved high accuracy and outperformed traditional non-sequence-based machine learning approaches on a dataset of over 45k emergency care patients.
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.