{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:30:02Z","timestamp":1775759402918,"version":"3.50.1"},"reference-count":29,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Pharmacovigilance is vital for post-market drug safety monitoring. Traditional trials inadequately capture adverse reactions. Patient-generated opinions offer valuable insights but pose challenges due to limited availability, inefficient annotation scheme, and volume of drug reviews, indicating the need of automation. To address the challenge posed by the limited availability of annotated drug reviews, we introduce a novel mechanism of annotating drug reviews dataset through human intervention, specifically tailored for Aspect Term Extraction (ATE) and Polarity Detection (PD) in three medical conditions: Depression, Arthritis and Birth control, thereby making it publicly available to facilitate future research. We first designed, ATE\n                    <jats:sc>drug<\/jats:sc>\n                    , an automated annotation scheme with minimal human intervention by employing an expert-driven rule-based approach. We further deploy ATE\n                    <jats:sc>drug<\/jats:sc>\n                    for sequence labelling and classification tasks in drug reviews. The automatically labeled data is further used for training and testing of deep learning models: BERT, BioBERT, and ClinicalBERT, for clinical use. We manually evaluate the results through annotator agreement to validate the effectiveness of ATE\n                    <jats:sc>drug<\/jats:sc>\n                    . The labeled dataset is further used to construct transformer-based models. Our proposed model is reliable, safe and trustworthy for healthcare domain, thereby eliminating the compromises with hallucination and data security through generative AI models. All experimental codes and data are available on GitHub (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/CMOONCS\/ateDrug.git\" xlink:type=\"simple\">https:\/\/github.com\/CMOONCS\/ateDrug.git<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0344296","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:34:57Z","timestamp":1775756097000},"page":"e0344296","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["ATEdrug: A reliable human-in-the-loop annotation scheme for aspect term extraction and polarity detection in drug reviews"],"prefix":"10.1371","volume":"21","author":[{"given":"Gunjan","family":"Ansari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandni","family":"Saxena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahab Saquib","family":"Sohail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0559-8289","authenticated-orcid":true,"given":"Zain","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aziz","family":"Sheikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"issue":"4","key":"pone.0344296.ref001","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s40264-023-01386-1","article-title":"The Critical Intersect of Regulations, Health Technology Assessment, and Drug Safety Assessments","volume":"47","author":"W-W Yang","year":"2023","journal-title":"Drug Saf"},{"key":"pone.0344296.ref002","unstructured":"Vijayaraghavan S, Basu D. Sentiment analysis in drug reviews using supervised machine learning algorithms. arXiv preprint arXiv:200311643. 2020."},{"key":"pone.0344296.ref003","doi-asserted-by":"crossref","unstructured":"Punith N, Raketla K. Sentiment analysis of drug reviews using transfer learning. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). 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Deep Learning Techniques for Aspect Based Sentiment Analysis. In: 2022 14th International Conference on Computer Research and Development (ICCRD). IEEE; 2022. p. 69\u201373.","DOI":"10.1109\/ICCRD54409.2022.9730443"},{"key":"pone.0344296.ref021","doi-asserted-by":"crossref","first-page":"110025","DOI":"10.1016\/j.knosys.2022.110025","article-title":"Integrating external knowledge into aspect-based sentiment analysis using graph neural network","volume":"259","author":"T Gu","year":"2023","journal-title":"Knowl-Based Syst"},{"key":"pone.0344296.ref022","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.aci.2019.02.002","article-title":"A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis","volume":"18","author":"P Ray","year":"2020","journal-title":"ACI"},{"key":"pone.0344296.ref023","doi-asserted-by":"crossref","unstructured":"Gr\u00e4\u00dfer F, Kallumadi S, Malberg H, Zaunseder S. 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