A review of recent work in transfer learning and domain adaptation for natural language processing of electronic health records
Published in Yearbook of medical informatics 30 (01), 239-244, 2021, 2021
Recommended citation: Egoitz Laparra, Aurelie Mascio, Sumithra Velupillai, and Timothy Miller. 2021. A review of recent work in transfer learning and domain adaptation for natural language processing of electronic health records. In Yearbook of medical informatics 30 (01), 239-244, 2021. https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0041-1726522
Abstract:
Objectives: We survey recent work in biomedical NLP on building more adaptable or generalizable models, with a focus on work dealing with electronic health record (EHR) texts, to better understand recent trends in this area and identify opportunities for future research. Methods: We searched PubMed, the Institute of Electrical and Electronics Engineers (IEEE), the Association for Computational Linguistics (ACL) anthology, the Association for the Advancement of Artificial Intelligence (AAAI) proceedings, and Google Scholar for the years 2018-2020. We reviewed abstracts to identify the most relevant and impactful work, and manually extracted data points from each of these papers to characterize the types of methods and tasks that were studied, in which clinical domains, and current state-of-the-art results. Results: The ubiquity of pre-trained transformers in clinical NLP research has contributed to an …