Evidence-Based Clinical Research Laboratory, Department of Health Science and Clinical Pharmacy, Chung-Ang University, Seoul, Republic of Korea.
Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, Institut Teknologi Bandung, Bandung, Indonesia.
J Med Internet Res. 2023 Aug 16;25:e45146. doi: 10.2196/45146.
Methylphenidate is an effective first-line treatment for attention-deficit/hyperactivity disorder (ADHD). However, many adverse effects of methylphenidate have been recorded from randomized clinical trials and patient-reported outcomes, but it is difficult to determine abuse from them. In the context of COVID-19, it is important to determine how drug use evaluation, as well as misuse of drugs, have been affected by the pandemic. As people share their reasons for using medication, patient sentiments, and the effects of medicine on social networking services (SNSs), the application of machine learning and SNS data can be a method to overcome the limitations. Proper machine learning models could be evaluated to validate the effects of the COVID-19 pandemic on drug use.
To analyze the effect of the COVID-19 pandemic on the use of methylphenidate, this study analyzed the adverse effects and nonmedical use of methylphenidate and evaluated the change in frequency of nonmedical use based on SNS data before and after the outbreak of COVID-19. Moreover, the performance of 4 machine learning models for classifying methylphenidate use based on SNS data was compared.
In this cross-sectional study, SNS data on methylphenidate from Twitter, Facebook, and Instagram from January 2019 to December 2020 were collected. The frequency of adverse effects, nonmedical use, and drug use before and after the COVID-19 pandemic were compared and analyzed. Interrupted time series analysis about the frequency and trends of nonmedical use of methylphenidate was conducted for 24 months from January 2019 to December 2020. Using the labeled training data set and features, the following 4 machine learning models were built using the data, and their performance was evaluated using F- scores: naïve Bayes classifier, random forest, support vector machine, and long short-term memory.
This study collected 146,352 data points and detected that 4.3% (6340/146,352) were firsthand experience data. Psychiatric problems (521/1683, 31%) had the highest frequency among the adverse effects. The highest frequency of nonmedical use was for studies or work (741/2016, 36.8%). While the frequency of nonmedical use before and after the outbreak of COVID-19 has been similar (odds ratio [OR] 1.02 95% CI 0.91-1.15), its trend has changed significantly due to the pandemic (95% CI 2.36-22.20). Among the machine learning models, RF had the highest performance of 0.75.
The trend of nonmedical use of methylphenidate has changed significantly due to the COVID-19 pandemic. Among the machine learning models using SNS data to analyze the adverse effects and nonmedical use of methylphenidate, the random forest model had the highest performance.
哌醋甲酯是治疗注意缺陷多动障碍（ADHD）的有效一线药物。然而，从随机临床试验和患者报告的结果中记录了哌醋甲酯的许多不良反应，但很难从中确定滥用情况。在 COVID-19 背景下，确定药物评估以及药物滥用如何受到大流行的影响非常重要。随着人们分享使用药物的原因、患者的情绪以及药物对社交网络服务（SNS）的影响，机器学习和 SNS 数据的应用可以成为克服这些限制的一种方法。适当的机器学习模型可以评估以验证 COVID-19 大流行对药物使用的影响。
分析 COVID-19 大流行对哌醋甲酯使用的影响，本研究分析了哌醋甲酯的不良反应和非医疗用途，并根据 COVID-19 爆发前后的 SNS 数据评估了非医疗使用频率的变化。此外，比较了基于 SNS 数据对哌醋甲酯使用进行分类的 4 种机器学习模型的性能。
在这项横断面研究中，从 2019 年 1 月至 2020 年 12 月，从 Twitter、Facebook 和 Instagram 上收集了关于哌醋甲酯的 SNS 数据。比较并分析了 COVID-19 大流行前后药物不良反应、非医疗使用和药物使用的频率。对 2019 年 1 月至 2020 年 12 月的 24 个月进行了关于哌醋甲酯非医疗使用频率和趋势的中断时间序列分析。使用标记的训练数据集和特征，使用数据构建了以下 4 种机器学习模型，并使用 F-分数评估了它们的性能：朴素贝叶斯分类器、随机森林、支持向量机和长短期记忆。
本研究共收集了 146352 个数据点，并检测到其中 4.3%（6340/146352）是第一手经验数据。在不良反应中，精神问题（521/1683，31%）的发生率最高。非医疗用途的最高频率是用于学习或工作（741/2016，36.8%）。虽然 COVID-19 爆发前后非医疗使用的频率相似（比值比[OR]1.02，95%置信区间[CI]0.91-1.15），但其趋势由于大流行而发生了显著变化（95%CI2.36-22.20）。在机器学习模型中，RF 的性能最高，为 0.75。
由于 COVID-19 大流行，哌醋甲酯的非医疗使用趋势发生了显著变化。在使用 SNS 数据分析哌醋甲酯的不良反应和非医疗使用的机器学习模型中，随机森林模型的性能最高。
