April 2026
Intermediate
586 pages
17h 40m
French
Content preview from Apprendre AutoML (French Edition)
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Chapitre 16. Étudede cas n° 3 : Santé —Prédiction des réadmissions de patients
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Au chapitre 14, on a abordé la détection de la fraude en temps réel, avec ses exigences de latence de l'ordre de la milliseconde. Au chapitre 15, on a exploré l' de prévision de la demande à très grande échelle, sur des millions de combinaisons de références et de magasins. On aborde maintenant le domaine le plus sensible de tous : la santé, où les décisions algorithmiques ont un impact direct sur la vie des patients, où la conformité réglementaire est non négociable, et où l'équité n'est pas juste un plus, mais un impératif éthique.
Remarque
Tous les exemples de code de ce chapitre ont été testés et validés avec AutoGluon 1.4.0. Pour les implémentations complètes, les résultats détaillés, les variantes expérimentales et les techniques avancées supplémentaires, consulte le notebook Jupyter d'accompagnement Supplementary_Code_C_Healthcare_Readmission.ipynb.
AutoGluon est en cours de développement. Rends-toi sur le site web d'AutoGluon pour obtenir la documentation et les instructions d'installation les plus récentes.
Le défi commercial
HealthFirst Medical Center, un réseau hospitalier universitaire de 450 lits accueillant 85 000 patients hospitalisés chaque année, était confronté à un problème coûteux : les réadmissions dans les 30 jours. Lorsque les patients reviennent ...
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