PMID- 37847653
OWN - NLM
STAT- MEDLINE
DCOM- 20231225
LR  - 20240314
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 31
IP  - 1
DP  - 2023 Dec 22
TI  - 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).
PG  - 70-78
LID - 10.1093/jamia/ocad197 [doi]
AB  - 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.
CI  - Published by Oxford University Press on behalf of the American Medical 
      Informatics Association 2023.
FAU - Konkel, Karen
AU  - Konkel K
AUID- ORCID: 0000-0001-6129-9120
AD  - 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.
AD  - Department of Health Services Administration, School of Health Professions, The 
      University of Alabama at Birmingham, Birmingham, AL 35233, United States.
FAU - Oner, Nurettin
AU  - Oner N
AUID- ORCID: 0000-0002-4761-7863
AD  - Department of Health Services Administration, School of Health Professions, The 
      University of Alabama at Birmingham, Birmingham, AL 35233, United States.
FAU - Ahmed, Abdulaziz
AU  - Ahmed A
AUID- ORCID: 0000-0001-6081-8507
AD  - Department of Health Services Administration, School of Health Professions, The 
      University of Alabama at Birmingham, Birmingham, AL 35233, United States.
FAU - Jones, S Christopher
AU  - Jones SC
AUID- ORCID: 0000-0002-0478-0978
AD  - 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.
FAU - Berner, Eta S
AU  - Berner ES
AUID- ORCID: 0000-0003-4319-2949
AD  - Department of Health Services Administration, School of Health Professions, The 
      University of Alabama at Birmingham, Birmingham, AL 35233, United States.
AD  - Informatics Institute, The University of Alabama at Birmingham, Birmingham, AL 
      35294, United States.
FAU - Zengul, Ferhat D
AU  - Zengul FD
AUID- ORCID: 0000-0002-8454-1335
AD  - Department of Health Services Administration, School of Health Professions, The 
      University of Alabama at Birmingham, Birmingham, AL 35233, United States.
AD  - Informatics Institute, The University of Alabama at Birmingham, Birmingham, AL 
      35294, United States.
AD  - Electrical & Computer Engineering, The Center for Integrated Systems, The 
      University of Alabama at Birmingham, Birmingham, AL 35294, United States.
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
EIN - J Am Med Inform Assoc. 2023 Nov 20;:. PMID: 37986619
MH  - United States
MH  - Humans
MH  - *Adverse Drug Reaction Reporting Systems
MH  - Natural Language Processing
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Software
MH  - United States Food and Drug Administration
MH  - Cough
PMC - PMC10746310
OTO - NOTNLM
OT  - OTC drugs
OT  - adverse drug event
OT  - consumer preferences
OT  - drug safety
OT  - homeopathic remedies
OT  - natural language processing
COIS- None declared.
EDAT- 2023/10/17 18:43
MHDA- 2023/12/25 06:42
PMCR- 2024/10/17
CRDT- 2023/10/17 13:13
PHST- 2023/06/27 00:00 [received]
PHST- 2023/09/19 00:00 [revised]
PHST- 2023/10/10 00:00 [accepted]
PHST- 2024/10/17 00:00 [pmc-release]
PHST- 2023/12/25 06:42 [medline]
PHST- 2023/10/17 18:43 [pubmed]
PHST- 2023/10/17 13:13 [entrez]
AID - 7320056 [pii]
AID - ocad197 [pii]
AID - 10.1093/jamia/ocad197 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2023 Dec 22;31(1):70-78. doi: 10.1093/jamia/ocad197.