Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing
11 September 2017 (online)
Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP).
Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers.
Results: The five clinical NLP best papers provide a contribution that ranges from emerging original foundational methods to transitioning solid established research results to a practical clinical setting. They offer a framework for abbreviation disambiguation and coreference resolution, a classification method to identify clinically useful sentences, an analysis of counseling conversations to improve support to patients with mental disorder and grounding of gradable adjectives.
Conclusions: Clinical NLP continued to thrive in 2016, with an increasing number of contributions towards applications compared to fundamental methods. Fundamental work addresses increasingly complex problems such as lexical semantics, coreference resolution, and discourse analysis. Research results translate into freely available tools, mainly for English.
Clinical Natural Language Processing
Althoff, T, Clark K, Leskovec, J. Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health. Trans Assoc Comput Linguist 2016(4):463-76 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361062/
Kilicoglu, H, Demner-Fushman, D. Bio-SCoRes: A Smorgasbord Architecture for Coreference Resolution in Biomedical Text. PLoS One. 2016 Mar 2;11(3):e0148538 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0148538
Morid, MA, Fiszman, M, Raja, K, Jonnalagadda, SR, Del Fiol, G. Classification of clinically useful sentences in clinical evidence resources. J Biomed Inform. 2016 Apr;60:14-22 http://www.sciencedirect.com/science/article/pii/S1532046416000046?via%3Dihub
Shivade C, de Marneffe MC, Fosler-Lussier E, Lai AM. Identification, characterization, and grounding of gradable terms in clinical text. Proceedings of the 15th Workshop on Biomedical Natural Language Processing. 2016:17-26 https://www.semanticscholar.org/paper/Identification-characterization-and-grounding-of-g-Shivade-Marneffe/c00ba120de1964b444807255030741d199ba6e04
Wu, Y, Denny, JC, Rosenbloom, ST, Miller, RA, Giuse, DA, Wang, L, Blanquicett, C, Soysal, E, Xu, J, Xu, H. A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD). J Am Med Inform Assoc 2017 Apr 1;24(e1):e79-e86 https://academic.oup.com/jamia/article-abstract/24/e1/e79/2631496/A-long-journey-to-short-abbreviations-developing?redirectedFrom=fulltext