Zolnour Ali, Eldredge Christina E, Faiola Anthony, Yaghoobzadeh Yadollah, Khani Masoud, Foy Doreen, Topaz Maxim, Kharrazi Hadi, Fung Kin Wah, Fontelo Paul, Davoudi Anahita, Tabaie Azade, Breitinger Scott A, Oesterle Tyler S, Rouhizadeh Masoud, Zonnor Zahra, Moen Hans, Patrick Timothy B, Zolnoori Maryam
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
School of Information, University of South Florida, Tampa, FL, United States.
Front Artif Intell. 2023 Aug 24;6:1229609. doi: 10.3389/frai.2023.1229609. eCollection 2023.
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.
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.
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.
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.
30%至68%的患者过早停用抗抑郁药物治疗，这对患者安全和医疗保健结果构成重大风险。在线医疗论坛有可能提供丰富且独特的数据源，揭示抗抑郁药物停药的一些维度，而这些维度可能是传统数据源无法捕捉到的。
我们分析了在线医疗论坛“askapatient.com”上的891篇患者叙述，利用内容分析创建了PsyRisk——一个突出与抗抑郁药物停药相关风险因素的语料库。利用PsyRisk以及PsyTAR[一个与抗抑郁药物相关的药物不良反应（ADR）的公开可用语料库]，我们开发了一种机器学习驱动的算法，用于主动识别有突然停用抗抑郁药物风险的患者。
在分析的891名患者中，有232人报告停用了抗抑郁药物。在这些患者中，92%经历了药物不良反应，72%的人发现这些反应令人痛苦，对他们的日常活动产生了负面影响。约26%的患者认为抗抑郁药物无效。报告的大多数药物不良反应是生理性的（61%，411/673），其次是认知性的（30%，197/673）和心理性的（28%，188/673）药物不良反应。在我们的研究中，我们采用了嵌套交叉验证策略，外层进行5折交叉验证用于模型选择，内层进行5折交叉验证用于超参数调整。通过这种强大的验证技术评估，我们的风险识别算法的性能产生了90.77的AUC-ROC和83.33的F1分数。突然停药的最主要因素是药物不良反应带来的高度痛苦感和抗抑郁药物的无效感。
本研究中确定的风险因素和开发的风险识别算法具有很大的临床应用潜力。它们可以帮助医疗保健专业人员识别和管理有过早停用抗抑郁药物治疗风险的抑郁症患者。
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