A scoping review of publicly available language tasks in clinical natural language processing. (3rd August 2022)

• Record Type: Journal Article
• Authors: Gao, Yanjun; Dligach, Dmitriy; Christensen, Leslie; Tesch, Samuel; Laffin, Ryan; Xu, Dongfang; Miller, Timothy; Uzuner, Ozlem; Churpek, Matthew M; Afshar, Majid
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 10(2022)
• Page Start: 1797
• Page End: 1806
• Publication Date: 2022-08-03
• Subjects: natural language processing -- clinical informatics -- electronic health records -- systematic review -- clinical decision support; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac127
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods: We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines. Results: A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type. Discussion: The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation. Conclusion: The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.