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挖掘药物不良反应的挑战与机遇:来自制药、监管机构、医疗保健提供者和消费者的观点。

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

机构信息

Department of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., West Hollywood, CA 90069, USA.

Life Sciences-Text Mining, Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain.

出版信息

Database (Oxford). 2022 Sep 2;2022. doi: 10.1093/database/baac071.

Abstract

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.

摘要

监测药物安全性是整个药物生命周期的核心关注点。在这个生命周期的每个阶段都会产生关于毒性和不良事件的信息,各利益相关方都非常有兴趣应用文本挖掘和人工智能 (AI) 方法来管理不断增加的信息量。为了认识到这些应用的重要性,以及评估挑战在推动文本挖掘方面的作用,BioCreative VII(生物学信息提取的关键性评估)的组织者召集了一组专家,探讨“挖掘药物不良反应的挑战”。本文是专家组讨论的成果;每位小组成员都根据自己的研究和组织文本挖掘挑战评估的经验,重点介绍了特定的文本挖掘应用。虽然这些突出的应用仅代表了这个问题空间的复杂性,但它们揭示了文本挖掘在帮助药物发现、测试、营销和上市后监测的复杂过程中的机会和挑战。利益相关方渴望接受自然语言处理和 AI 工具来帮助处理这个过程,前提是这些工具可以被证明对利益相关方的工作流程有价值。这为 BioCreative 社区提供了一个机会,与监管机构、制药公司和文本挖掘社区合作,确定未来挑战评估的下一步措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c54/9436770/db8efd35dfd4/baac071f1.jpg

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