Yearb Med Inform 2017; 26(01): 214-227
DOI: 10.15265/IY-2017-029
Section 10: Natural Language Processing
Survey
Georg Thieme Verlag KG Stuttgart

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

G. Gonzalez-Hernandez1, A. Sarker1, K. O’Connor1, G. Savova2
  • 1Department of Epidemiology, Biostatistics, and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  • 2Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA
Further Information

Publication History

Publication Date:
11 September 2017 (online)

Summary

Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts.

Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts.

Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data.

Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.