Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare

OBJECTIVE To summarize recent research and present a selection of the best papers published in 2014 in the field of clinical Natural Language Processing (NLP). METHOD 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. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers. RESULTS The clinical NLP best paper selection shows that the field is tackling text analysis methods of increasing depth. The full review process highlighted five papers addressing foundational methods in clinical NLP using clinically relevant texts from online forums or encyclopedias, clinical texts from Electronic Health Records, and included studies specifically aiming at a practical clinical outcome. The increased access to clinical data that was made possible with the recent progress of de-identification paved the way for the scientific community to address complex NLP problems such as word sense disambiguation, negation, temporal analysis and specific information nugget extraction. These advances in turn allowed for efficient application of NLP to clinical problems such as cancer patient triage. Another line of research investigates online clinically relevant texts and brings interesting insight on communication strategies to convey health-related information. CONCLUSIONS The field of clinical NLP is thriving through the contributions of both NLP researchers and healthcare professionals interested in applying NLP techniques for concrete healthcare purposes. Clinical NLP is becoming mature for practical applications with a significant clinical impact.