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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective
Jérémie Despraz1, Antoine Garnier2, Marie Méan2
1Groupe Data Science, Direction des systèmes d'information, Département infrastructures, Centre hospitalier universitaire vaudois, 1011 Lausanne.
Abstract:
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.
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