Improving length of stay prediction using a hidden Markov model.

Estimating length of stay of intensive care unit patients is crucial to reducing health care costs. This can help physicians intervene at the right time to prevent adverse outcomes for the patients. Moreover, resource allocation can be optimized to ensure appropriate hospital staff levels. Yet the length of stay prediction is very hard, as physicians can only accurately estimate half of their patient population. As electronic health records have become more prevalent, researchers can harness the power of machine learning to accurately predict the length of stay. We propose a hidden Markov model-based framework to predict the length of stay using some of patients' physiological measurements during the first 48 hours of their admission to the intensive care unit. We show that this model can succinctly capture temporal patient representations. We demonstrate the potential of our framework on real ICU data in consistently outperforming most of the existing baselines.