Modeling and Predicting Patient Length of Stay: A Survey

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance. Research has focused on prediction of measurable outcomes, including mortality, risk of complications and length of hospital stay. Length of stay is an important metric both for healthcare providers and patients, and is influenced by numerous factors. In particular length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the critical care unit. This paper examines a range of length of stay applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay prediction. Moreover, the paper provides a classification for the analytical methods to length of stay prediction associated with a grouping of relevant research papers published in the domain. This study focuses on publications from the year 1984 till 2015 related to the domain of survival analysis and length of stay prediction. In addition, the paper highlights some of the gaps and challenges of the