Title: Detection of adverse drug reactions from online health communities' data: a case study of anti-epileptic drugs

Authors: Anwar Ali Yahya

Addresses: Department of Computer Science, Najran University, Najran, Saudi Arabia

Abstract: This paper investigates the problem of detecting adverse drug reactions of anti-epileptic drugs from patients' reviews in online health communities. A lexicon-based methodology is proposed and applied to a dataset of patients' reviews collected from two online health communities. The dataset is cleaned and the adverse reactions of anti-epileptic drugs are extracted with the aid of consumer health vocabulary and a lexicon of adverse drug reactions. A proportional reporting ratio is then applied to quantify the correlation between each drug and adverse reactions and thus identify the adverse reactions of each drug. The results are validated quantitatively against a database of adverse drug reactions, called side effect resource, and qualitatively against the extant knowledge related to the common adverse reactions and drug-drug similarities of anti-epileptic drugs. The validation results provide evidences on the effectiveness of the proposed methodology and the validity of online health communities' data for adverse drug reactions detection.

Keywords: adverse drug reaction detection; pharmacovigilance; anti-epileptic drugs; AEDs; data mining; online health communities.

DOI: 10.1504/IJMEI.2025.145039

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.2, pp.157 - 172

Received: 02 Jan 2022
Accepted: 09 Jul 2022
Published online: 18 Mar 2025 *

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