Inappropriate medication use can lead to adverse drug events (ADEs) that may be misinterpreted as a new medical condition. This misinterpretation can lead to a prescribing cascade, where the condition is treated with another drug, which can lead to another ADE, and so on. However, the prevalence of prescribing cascade is unknown and its impact on patient care can be hard to assess if done uniquely through manual chart reviews. This project aims to develop a deep learning method for automatically detecting prescribing cascades in clinical notes.



  • James Liang

Année : 2022

Région : Montreal Regional Science & Technology Fair (Sec/Coll)

Type de projet : C

Classe : C1

Categorie de projet : IIR

Volet : Collegial

Niveau scolaire : Collégial 1

École : Marianopolis College

Prix et distinctions

Finale Québécoise


  • Médaille du Réseau Technoscience - Argent Collégial
  • Prix de IEEE Canadian Foundation - Éloi Ngandui


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