Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

S. Velupillai; D. Mowery; B. R. South; M. Kvist; H. Dalianis

doi:10.15265/IY-2015-009

Yearbook of Medical Informatics, Inhaltsverzeichnis

Yearb Med Inform 2015; 24(01): 183-193
DOI: 10.15265/IY-2015-009

Original Article

Georg Thieme Verlag KG Stuttgart

Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis

Autoren

S. Velupillai

¹Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden
D. Mowery

²Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
B. R. South

²Department of Biomedical Informatics, University of Utah, Salt Lake City, USA
M. Kvist

¹Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden

³Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Sweden
H. Dalianis

¹Department of Computer and Systems Sciences, (DSV), Stockholm University, Stockholm, Sweden

Abstract

Summary

Objectives: We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis.

Methods: We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers.

Results: Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications.

Conclusions: There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.

Keywords

Clinical Natural Language Processing - Semantics - Information Extraction - Annotation, Domain Adaptation - Review

Volltext

Referenzen

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