Syrowatka Ania, Song Wenyu, Amato Mary G, Foer Dinah, Edrees Heba, Co Zoe, Kuznetsova Masha, Dulgarian Sevan, Seger Diane L, Simona Aurélien, Bain Paul A, Purcell Jackson Gretchen, Rhee Kyu, Bates David W
Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Lancet Digit Health. 2022 Feb;4(2):e137-e148. doi: 10.1016/S2589-7500(21)00229-6. Epub 2021 Nov 23.
Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.
药物不良事件（ADEs）是与医疗保健相关的伤害中最常见的类型之一，目前对其进行预测和检测的方式仍有很大的改进空间。我们进行了一项范围综述，以确定可以利用人工智能（AI）来降低ADEs发生频率的关键用例。我们重点关注现代机器学习技术和自然语言处理。该范围综述纳入了78篇文章。研究具有异质性，应用了各种AI技术，涵盖了广泛的药物和ADEs。我们确定了几个关键用例，通过预测预防ADEs以及早期检测减轻其影响，AI可以在这些用例中帮助降低ADEs的发生频率和后果。大多数研究（78项中的73项[94%]）评估了技术算法性能，很少有研究评估AI在临床环境中的应用。大多数文章（78项中的58项[74%]）是在过去5年内发表的，突出了一个新兴的研究领域。新型数据（如基因信息）的可用性以及对非结构化临床记录的获取可能会进一步推动该领域的发展。
