First published 9/30/2026, 9:09:15 AM · Last updated 9/30/2026, 9:09:15 AM

Analyse, anticipation et optimisation des parcours patient hospitaliers non-programmés à l’aide d’apprentissage supervisé et de la simulation à évènements discrets

Dissertation on analysis, anticipation and optimization of unscheduled hospital patient pathways using supervised learning and discrete-event simulation, published by Agence Bibliographique de l'Enseignement Supérieur on 2026-04-08. The source provides bibliographic metadata but no details on results, scope, or location.

Categories: public-health, technology, positive-progress

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 6/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 3/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 3/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The work addresses methods to optimize unscheduled hospital patient pathways using machine learning and simulation.

Sources

Analyse, anticipation et optimisation des parcours patient hospitaliers non-programmés à l’aide d’apprentissage supervisé et de la simulation à évènements discrets

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