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Review
. 2017 Aug;26(1):228-234.
doi: 10.15265/IY-2017-027. Epub 2017 Sep 11.

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

A Névéol, P Zweigenbaum
Review

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

A Névéol et al. Yearb Med Inform. 2017 Aug.

Abstract

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.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

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References

    1. In the reference list below, papers that were shortlisted as best paper candidates are marked with a *.

    1. Gonzalez-Hernandez G, Sarker A, O’Connor K, Savova G. Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text. Yearb Med Inform. 2017:214–27. - PMC - PubMed
    1. 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;6014–22. - PMC - PubMed
    1. 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.
    1. Kilicoglu H, Demner-Fushman D.Bio-SCoRes: A Smorgasbord Architecture for Coreference Resolution in Biomedical Text PLoS One 2016. Mar 2;1103e0148538.. - PMC - PubMed

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