Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.

Autor: Gonzalez-Hernandez G; Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., West Hollywood, CA 90069, USA., Krallinger M; Life Sciences-Text Mining, Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain., Muñoz M; Division of Pharmacovigilance, Office of Surveillance and Epidemiology, Center of Drug Evaluation and Research, FDA, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA., Rodriguez-Esteban R; Roche Innovation Center Basel, Roche Pharmaceuticals, Grenzacherstrasse 124, Basel 4070, Switzerland., Uzuner Ö; Information Sciences and Technology, George Mason University, 4400 University Dr, Fairfax, VA 22030, USA., Hirschman L; MITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA.
Jazyk: angličtina
Zdroj: Database : the journal of biological databases and curation [Database (Oxford)] 2022 Sep 02; Vol. 2022.
DOI: 10.1093/database/baac071
Abstrakt: Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.
(© The Author(s) 2022. Published by Oxford University Press.)
Databáze: MEDLINE
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