Machine learning in the prediction of medical inpatient length of stay.
artificial intelligence
deep learning
neural network
predictive analytics
Journal
Internal medicine journal
ISSN: 1445-5994
Titre abrégé: Intern Med J
Pays: Australia
ID NLM: 101092952
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
revised:
30
05
2020
received:
30
11
2019
accepted:
16
06
2020
pubmed:
24
10
2020
medline:
18
3
2022
entrez:
23
10
2020
Statut:
ppublish
Résumé
Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients. A review of machine learning in the prediction of total hospital LOS for medical inpatients was conducted using the databases PubMed, EMBASE and Web of Science. Of the 673 publications returned by the initial search, 21 articles met inclusion criteria. Of these articles the most commonly represented medical specialty was cardiology. Studies were also identified that had specifically evaluated machine learning LOS prediction in patients with diabetes and tuberculosis. The performance of the machine learning models in the identified studies varied significantly depending on factors including differing input datasets and different LOS thresholds and outcome metrics. Common methodological shortcomings included a lack of reporting of patient demographics and lack of reporting of clinical details of included patients. The variable performance reported by the studies identified in this review supports the need for further research of the utility of machine learning in the prediction of total inpatient LOS in medical patients. Future studies should follow and report a more standardised methodology to better assess performance and to allow replication and validation. In particular, prospective validation studies and studies assessing the clinical impact of such machine learning models would be beneficial.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
176-185Informations de copyright
© 2020 Royal Australasian College of Physicians.
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