Patient length of stay and mortality prediction: A survey

Awad, Aya; Bader–El–Den, Mohamed; McNicholas, James

doi:10.1177/0951484817696212

Cited by 128Machine Learning in HealthcareSepsis Diagnosis and TreatmentArtificial Intelligence in Healthcare

Patient length of stay and mortality prediction: A survey

Awad, Aya, Bader–El–Den, Mohamed, McNicholas, James·2017·Health Services Management Research

DOI 10.1177/0951484817696212

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Abstract

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 statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the 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 intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses 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 of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

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Current standards of estimating patient severity, such as the Acute Physiologic Assessment and Chronic Health Evaluation score, fail to incorporate medical characteristics beyond acute physiology.
“However, current standards of estimating patient severity, such as the Acute Physiologic Assessment and Chronic Health Evaluation score, fail to incorporate medical characteristics beyond acute physiology45.”
From Integrated multimodal artificial intelligence framework for healthcare applications (2022) · cites this paper as [45]

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Integrated multimodal artificial intelligence framework for healthcare applications
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Authors: Awad, A., Bader–El–Den, M., & McNicholas, J.
Affiliations: University of Portsmouth (GB)
School of Computing, University of Portsmouth, UK
Queen Alexandra Hospital (GB)
Corresponding author: Awad, Aya
ORCID: Awad, Aya
McNicholas, James
Venue: Health Services Management Research
Publisher: SAGE Publications
Published: 2017-03-22
Volume: 30
Issue: 2
Pages: 105-120
ISSN: 0951-4848 (ISSN-L)
1758-1044
DOI: 10.1177/0951484817696212
OpenAlex: W2597843262
Cited by: 128
References: 135
Citations by year: 2026: 12 · 2025: 13 · 2024: 21 · 2023: 14 · 2022: 19 · 2021: 23 · 2020: 12 · 2019: 12 · 2018: 1 · 2017: 1
FWCI: 4.24
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Health Services Management Research (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: length of stay prediction, mortality prediction, intensive care unit, intensive care, survival analysis, acute medicine, hospital performance, health care utilization, surrogate endpoints
MeSH terms: Critical Care, Humans, Intensive Care Units, Length of Stay, Prognosis, Surveys and Questionnaires, Hospital Mortality

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Patient length of stay and mortality prediction: A survey

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