A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries.
- 3 November 2012
- journal article
- research article
- Vol. 2012, 997-1003
Abstract
Clinical Natural Language Processing (NLP) systems extract clinical information from narrative clinical texts in many settings. Previous research mentions the challenges of handling abbreviations in clinical texts, but provides little insight into how well current NLP systems correctly recognize and interpret abbreviations. In this paper, we compared performance of three existing clinical NLP systems in handling abbreviations: MetaMap, MedLEE, and cTAKES. The evaluation used an expert-annotated gold standard set of clinical documents (derived from from 32 de-identified patient discharge summaries) containing 1,112 abbreviations. The existing NLP systems achieved suboptimal performance in abbreviation identification, with F-scores ranging from 0.165 to 0.601. MedLEE achieved the best F-score of 0.601 for all abbreviations and 0.705 for clinically relevant abbreviations. This study suggested that accurate identification of clinical abbreviations is a challenging task and that more advanced abbreviation recognition modules might improve existing clinical NLP systems.This publication has 32 references indexed in Scilit:
- Knowledge-based biomedical word sense disambiguation: comparison of approachesBMC Bioinformatics, 2010
- Discovering peripheral arterial disease cases from radiology notes using natural language processing.2010
- Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applicationsJournal of the American Medical Informatics Association, 2010
- An overview of MetaMap: historical perspective and recent advancesJournal of the American Medical Informatics Association, 2010
- Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE)Journal of the American Medical Informatics Association, 2010
- Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent ResearchYearbook of Medical Informatics, 2008
- Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized MedicineClinical Pharmacology & Therapeutics, 2008
- Automated Acquisition of Disease-Drug Knowledge from Biomedical and Clinical Documents: An Initial StudyJournal of the American Medical Informatics Association, 2008
- A study of abbreviations in clinical notes.2007
- Natural language processing to extract medical problems from electronic clinical documents: Performance evaluationJournal of Biomedical Informatics, 2006