A risk identification model for detection of patients at risk of antidepressant discontinuation

A risk identification model for detection of patients at risk of antidepressant discontinuation

PurposeBetween 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuat...

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Bibliographic Details
Main Authors: Ali Zolnour, Christina E. Eldredge, Anthony Faiola, Yadollah Yaghoobzadeh, Masoud Khani, Doreen Foy, Maxim Topaz, Hadi Kharrazi, Kin Wah Fung, Paul Fontelo, Anahita Davoudi, Azade Tabaie, Scott A. Breitinger, Tyler S. Oesterle, Masoud Rouhizadeh, Zahra Zonnor, Hans Moen, Timothy B. Patrick, Maryam Zolnoori
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-08-01
Series:Frontiers in Artificial Intelligence
Subjects:
antidepressant discontinuation
adverse drug events
antidepressant effectiveness
online healthcare forums
content analysis
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2023.1229609/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2023.1229609/full

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