Intelligence artificielle en médecine interne : développement d’un modèle prédictif des durées de séjour [Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective admissions]
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24 novembre 2021
- doi: 10.53738/REVMED.2021.17.760.2042
- issn: 1660-9379
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info:eu-repo/semantics/altIdentifier/doi/10.53738/REVMED.2021.17.760.2042
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info:eu-repo/semantics/altIdentifier/pmid/34817943
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info:eu-repo/semantics/altIdentifier/pissn/1660-9379
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_FC5BDD829CD30
info:eu-repo/semantics/openAccess , CC BY-NC-ND 4.0 , https://creativecommons.org/licenses/by-nc-nd/4.0/
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Human intelligence Intelligence Mind Patientes Sciences de la gestion Gestion, Sciences de la Sciences de gestion Techniques de gestion Direction Management Intelligence de l'homme Développement de l'intelligence Intelligence humaineCiter ce document
J. Despraz et al., « Intelligence artificielle en médecine interne : développement d’un modèle prédictif des durées de séjour [Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective admissions] », Serveur académique Lausannois, ID : 10.53738/REVMED.2021.17.760.2042
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Efficient management of hospitalized patients requires carefully planning each stay by taking into account patients' pathologies and hospital constraints. Therefore, the ability to accurately estimate length of stays allows for better interprofessional tasks coordination, improved patient flow management, and anticipated discharge preparation. This article presents how we built and evaluated a predictive model of length of stay based on clinical data available upon admission to a division of internal medicine. We show that Machine Learning-based approaches can predict lengths of stay with a similar level of accuracy as field experts.