Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

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Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).

アブストラクト	OBJECTIVE: Apply natural language processing (NLP) to Amazon consumer reviews to identify adverse events (AEs) associated with unapproved over the counter (OTC) homeopathic drugs and compare findings with reports to the US Food and Drug Administration Adverse Event Reporting System (FAERS). MATERIALS AND METHODS: Data were extracted from publicly available Amazon reviews and analyzed using JMP 16 Pro Text Explorer. Topic modeling identified themes. Sentiment analysis (SA) explored consumer perceptions. A machine learning model optimized prediction of AEs in reviews. Reports for the same time interval and product class were obtained from the FAERS public dashboard and analyzed. RESULTS: Homeopathic cough/cold products were the largest category common to both data sources (Amazon = 616, FAERS = 445) and were analyzed further. Oral symptoms and unpleasant taste were described in both datasets. Amazon reviews describing an AE had lower Amazon ratings (X2 = 224.28, P < .0001). The optimal model for predicting AEs was Neural Boosted 5-fold combining topic modeling and Amazon ratings as predictors (mean AUC = 0.927). DISCUSSION: Topic modeling and SA of Amazon reviews provided information about consumers' perceptions and opinions of homeopathic OTC cough and cold products. Amazon ratings appear to be a good indicator of the presence or absence of AEs, and identified events were similar to FAERS. CONCLUSION: Amazon reviews may complement traditional data sources to identify AEs associated with unapproved OTC homeopathic products. This study is the first to use NLP in this context and lays the groundwork for future larger scale efforts.
ジャーナル名	Journal of the American Medical Informatics Association : JAMIA
Pubmed追加日	2023/10/17
投稿者	Konkel, Karen; Oner, Nurettin; Ahmed, Abdulaziz; Jones, S Christopher; Berner, Eta S; Zengul, Ferhat D
組織名	Division of Pharmacovigilance, Office of Surveillance and Epidemiology, Center;for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring,;MD 20993, United States.;Department of Health Services Administration, School of Health Professions, The;University of Alabama at Birmingham, Birmingham, AL 35233, United States.;Informatics Institute, The University of Alabama at Birmingham, Birmingham, AL;35294, United States.;Electrical & Computer Engineering, The Center for Integrated Systems, The;University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Pubmed リンク	https://www.ncbi.nlm.nih.gov/pubmed/37847653/