The 2019 n2c2/UMass Lowell shared task on clinical concept normalization. (24th September 2020)

• Record Type: Journal Article
• Authors: Luo, Yen-Fu; Henry, Sam; Wang, Yanshan; Shen, Feichen; Uzuner, Ozlem; Rumshisky, Anna
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 10(2020)
• Page Start: 1529
• Page End: e1
• Publication Date: 2020-09-24
• Subjects: Natural language processing -- clinical narratives -- machine learning -- concept normalization; 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/ocaa106
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The n2c2/UMass Lowell spin-off shared task focused on medical concept normalization (MCN) in clinical records. This task aimed to assess state-of-the-art methods for matching salient medical concepts from clinical records to a controlled vocabulary. We describe the task and the dataset used, compare the participating systems, and identify the strengths and limitations of the current approaches and directions for future research. Materials and Methods: Participating teams were asked to link preselected text spans in discharge summaries (henceforth referred to as concept mentions) to the corresponding concepts in the SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and RxNorm vocabularies from the Unified Medical Language System. The shared task used the MCN corpus created by the organizers, which maps all mentions of problems, treatments, and tests in the 2010 i2b2/VA challenge data to the Unified Medical Language System concepts. Submitted systems represented 4 broad categories of approaches: cascading dictionary matching, cosine distance, deep learning, and retrieve-and-rank systems. Disambiguation modules were common across all approaches. Results: A total of 33 teams participated in the shared task. The best-performing team achieved an accuracy of 0.8526. The median and mean performances among all teams were 0.7733 and 0.7426, respectively. Conclusions: Overall performance among the top 10 teams was high. However, particularly challenging for all teams were mentions requiring disambiguation of misspelled words, acronyms, abbreviations, and mentions with more than 1 possible semantic type. Complex mentions of long, multiword terms were also challenging and, in the future, will require better methods for learning contextualized representations of concept mentions and better use of domain knowledge.