Patient length of stay and mortality prediction: a survey

Aya Awad, Mohamed Bader-El-Den, James McNicholas

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Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit (ICU) statistics by reducing the number of patients dying inside the ICU. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. Length of stay (LOS) is an important metric both for healthcare providers and patients, 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 ICU. LOS is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or ICU mortality. LOS is also a parameter, which has been used to identify severity of illness and healthcare resource utilisation.

This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods to length of stay and mortality prediction associated with a grouping of relevant research papers published in the year 1984 till 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.
Original languageEnglish
Pages (from-to)105-120
Number of pages16
JournalHealth Services Management Research
Issue number2
Early online date22 Mar 2017
Publication statusPublished - 1 May 2017


  • mortality prediction
  • patient length of stay


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