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Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.
S Velupillai1, D Mowery, B R South
1Sumithra Velupillai, Department of Computer and Systems Sciences, Stockholm University, Postbox 7003, 164 07 Kista, Sweden, Tel: +46 8 161 174, Fax: +46 8 703 9025,
Yearbook of Medical Informatics
|August 22, 2015
Summary
Recent advances in clinical Natural Language Processing (NLP) focus on semantic analysis. Key subtasks like corpus creation and meaning extraction are improving, nearing human performance, but clinical application lags.
Area of Science:
- Medical Informatics
- Computational Linguistics
Background:
- Clinical Natural Language Processing (NLP) is crucial for extracting information from unstructured health data.
- Semantic analysis in clinical NLP aims to understand the meaning within clinical text.
Purpose of the Study:
- To review recent advances in clinical NLP, focusing on semantic analysis and its supporting subtasks.
- To identify key developments and future directions in clinical NLP research.
Main Methods:
- A literature review of clinical NLP research published between 2008 and 2014.
- Emphasis on recent publications (2012-2014) from PubMed, ACL proceedings, and cited references.
Main Results:
- Significant progress in NLP subtasks supporting semantic analysis, including corpus creation, de-identification, and meaning extraction (morphological, syntactic, semantic).
- Clinical NLP performance is approaching human agreement levels.
- Development of NLP tools and approaches is boosted by annotated corpora.
- Growing research in non-English clinical NLP.
Conclusions:
- Clinical NLP semantic analysis shows significant advancements.
- A gap persists between advanced NLP resource development and real-world clinical implementation.
- Emerging clinical use cases are driven by healthcare initiatives and patient-generated data from social media and devices.
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