Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.
OBJECTIVE
To summarize recent research and present a selection of the best papers published in 2015 in the field of clinical Natural Language Processing (NLP).


METHODS
A systematic review of the literature was performed by the two section editors of the IMIA Yearbook NLP section by searching bibliographic databases with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Section editors first selected a shortlist of candidate best papers that were then peer-reviewed by independent external reviewers.


RESULTS
The clinical NLP best paper selection shows that clinical NLP is making use of a variety of texts of clinical interest to contribute to the analysis of clinical information and the building of a body of clinical knowledge. The full review process highlighted five papers analyzing patient-authored texts or seeking to connect and aggregate multiple sources of information. They provide a contribution to the development of methods, resources, applications, and sometimes a combination of these aspects.


CONCLUSIONS
The field of clinical NLP continues to thrive through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques to impact clinical practice. Foundational progress in the field makes it possible to leverage a larger variety of texts of clinical interest for healthcare purposes.
Aurélie Névéol, LIMSI CNRS UPR 3251, Université Paris Saclay, Rue John von Neumann, 91400 Orsay, France, E-mail: neveol@limsi.fr.
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Névéol
Pierre Zweigenbaum, LIMSI CNRS UPR 3251, Université Paris Saclay, Rue John von Neumann, 91400 Orsay, France, E-mail: pz@limsi.fr.
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Zweigenbaum
D000465:Algorithms; D057225:Data Mining; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D018579:Patient Selection; D017432:Unified Medical Language System
Awards and prizes; computer-assisted; decision making; medical informatics/trends; natural language processing; semantics