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This paper is in the following e-collection/theme issue:

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (2003) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1053) Infodemiology and Infoveillance (1618) Decision Support for Health Professionals (1712) Substance Abuse (573) Machine Learning (2327) Early Results from COVID-19 Studies (373)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45146, first published .
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The Adverse Effects and Nonmedical Use of Methylphenidate Before and After the Outbreak of COVID-19: Machine Learning Analysis

The Adverse Effects and Nonmedical Use of Methylphenidate Before and After the Outbreak of COVID-19: Machine Learning Analysis

Authors of this article:

Hocheol Shin1 Author Orcid Image ;   Cindra Tri Yuniar2 Author Orcid Image ;   SuA Oh1 Author Orcid Image ;   Sujata Purja1 Author Orcid Image ;   Sera Park1 Author Orcid Image ;   Haeun Lee1 Author Orcid Image ;   Eunyoung Kim1, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (5) Metrics

Journals

  1. Islam M, Islam M, Shourav N, Rahman I, Faisal F, Islam M, Sarker I. Exploring post-COVID-19 health effects and features with advanced machine learning techniques. Scientific Reports 2024;14(1) View
  2. Van Wichelen N, Boogaerts T, Raeime A, Ferreira L, Gys C, Covaci A, van Nuijs A, De Loof H. Longitudinal and Seasonal Trends in Reimbursed and Non‐Reimbursed Methylphenidate Dispense and Its Most Common Alternatives in Belgium. Pharmacoepidemiology and Drug Safety 2026;35(4) View
  3. Van Wichelen N, Boogaerts T, Van Hal G, Fransen E, Gys C, Covaci A, van Nuijs A. Academic Performance Pressure and Methylphenidate Consumption: Insights From Wastewater‐Based Epidemiology. Drug Testing and Analysis 2026 View