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

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

Research output: Contribution to journalArticlepeer-review


Purpose: Between 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 discontinuation that may not be captured by conventional data sources. Methods: We analyzed 891 patient narratives from the online healthcare forum, “askapatient.com,” utilizing content analysis to create PsyRisk—a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

Original languageEnglish (US)
Article number1229609
JournalFrontiers in Artificial Intelligence
StatePublished - 2023


  • adverse drug events
  • antidepressant discontinuation
  • antidepressant effectiveness
  • content analysis
  • machine learning
  • online healthcare forums

ASJC Scopus subject areas

  • Artificial Intelligence

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