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

Abstract

SummaryObjective: 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.

Customization scenarios for de-identification of clinical notes

“…For example, between 2007 and 2018, the number of PubMed records with "free text" or "unstructured text" more than tripled [2]. Advances in natural language processing and machine learning, and access to de-identified clinical datasets, have contributed to this increase [3].…”

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

“…The review by Meystre et al [5] presents an excellent overview of health-related text processing and its applications until 2007. The research presented in the present manuscript includes, for social media and clinical records, advances to fundamental NLP methods such as classification, concept extraction, and normalization published since 2008, mostly omitting what has been included in similar recent reviews [6][7][8], except when required to complete and highlight recent advances. For each data source, after reviewing advances in fundamental methods, we reviewed specific applications that capture the patient's perspective for specific conditions, treatments, or phenotypes.…”

“…For the applications, we built on the 2016 review by Demner-Fushman and Elhadad [7] and highlighted major achievements from 2013 to 2016 that are relevant to the patient's perspective focus of this review. The search and selection criteria used were similar to the ones used by Névéol and Zweigenbaum [8], from January 1st, 2013 through December 31st, 2016, resulting in 464 papers. A total of 62 papers focusing on clinical records were selected from this set.…”

Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing

“…We omit discussing basic research recently reviewed by Névéol and Zweigenbaum [3] that is however still needed and ongoing. Some examples include exciting new approaches proposed in the context of community challenges: 2012 i2b2 event and time extraction [4], 2014 i2b2/UTHealth modeling of risk factors for heart disease [5], ShARe SemEval 2014 recognition and normalization of disorders [6,7], and ShARe SemEval 2015 disorder and template filling shared tasks [8].…”

“…Since Névéol and Zweigenbaum provide information about methods [3], we only mention here that the methods in the included papers range from regular expressions that dominate research in social-media text processing, to event extraction in a supervised setting. More recently, there has been more and more progress in incorporating the principles of distributional semantics into an NLP pipeline, and a shift towards more semantic parsing, however more work in these areas is needed.…”