A Methodology for Estimating Hospital Intensive Care Unit Length of Stay Using Novel Machine Learning Tools

Batista, Roberto Williams; Sanchez-Arias, Reinaldo

doi:10.1109/icmla51294.2020.00135

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In contrast to the tabular structure of their data, we instead attempt to utilize the temporal dependencies within the data by structuring the patient data as medical event sequences. In Batista et al [4], patients are stratified into three categories (LOS < 3, 3 < LOS < 10, LOS >= 10) using RF and Support Vector Machines (SVM). The highest performance was achieved for RF models.…”

Section: Related Workmentioning

confidence: 99%

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Hospitalization Length of Stay Prediction using Patient Event Sequences

Hansen¹,

Nielsen²,

Mulvad³

et al. 2023

Preprint

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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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Section: Related Workmentioning

confidence: 99%

“…As we are only focusing on a single prediction task, we directly train model parameters and token embeddings toward the downstream task of LOS prediction without pre-training. The experimental code is available online 4 .…”

Section: Experimental Settingmentioning

confidence: 99%