Machine-learning prediction for hospital length of stay using a French medico-administrative database.
Machine learning
health services research
neural network
prediction
public health
Journal
Journal of market access & health policy
ISSN: 2001-6689
Titre abrégé: J Mark Access Health Policy
Pays: United States
ID NLM: 101670174
Informations de publication
Date de publication:
2023
2023
Historique:
entrez:
2
12
2022
pubmed:
3
12
2022
medline:
3
12
2022
Statut:
epublish
Résumé
Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS. Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90 Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values <0.0001). The performance of the RF, GB and NN models (AUC ranged from 0.808 to 0.810) was superior to that of the LR model (AUC = 0.795); all p-values <0.0001. In contrast, LR was superior to CART (AUC = 0.786), p < 0.0001. The variable most predictive of the PLOS was the destination of the patient after hospitalization to other institutions. The typical clinical profile of these patients (17.5% of the sample) was the elderly patient, admitted in emergency, for a trauma, a neurological or a cardiovascular pathology, more often institutionalized, with more comorbidities notably mental health problems, dementia and hemiplegia. The integration of ML, particularly the GB algorithm, may be useful for health-care professionals and bed managers to better identify patients at risk of PLOS. These findings underscore the need to strengthen hospitals through targeted allocation to meet the needs of an aging population.
Identifiants
pubmed: 36457821
doi: 10.1080/20016689.2022.2149318
pii: 2149318
pmc: PMC9707380
doi:
Types de publication
Journal Article
Langues
eng
Pagination
2149318Informations de copyright
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Déclaration de conflit d'intérêts
No potential conflict of interest was reported by the authors.
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