| A flexible framework for deriving assertions from electronic medical records. | |
| | |
MedLine Citation:
|
PMID: 21724741 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
|
Objective This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest. Materials and methods The authors used two machine-learning (ML) classifiers: support vector machines (SVMs) and conditional random fields (CRFs). Because SVMs and CRFs can operate on a large set of features extracted from both clinical texts and external resources, the authors address the following research question: Which features need to be selected for obtaining optimal results? To this end, the authors devise feature-selection techniques which greatly reduce the amount of manual experimentation and improve performance. Results The authors evaluated their approaches on the 2010 i2b2/VA challenge data. Concept extraction achieves 79.59 micro F-measure. Assertion classification achieves 93.94 micro F-measure. Discussion Approaching medical concept extraction and assertion classification through ML-based techniques has the advantage of easily adapting to new data sets and new medical informatics tasks. However, ML-based techniques perform best when optimal features are selected. By devising promising feature-selection techniques, the authors obtain results that outperform the current state of the art. Conclusion This paper presents two ML-based approaches for processing language in the clinical texts evaluated in the 2010 i2b2/VA challenge. By using novel feature-selection methods, the techniques presented in this paper are unique among the i2b2 participants. |
| | |
Authors:
|
Kirk Roberts; Sanda M Harabagiu |
Publication Detail:
|
Type: JOURNAL ARTICLE Date: 2011-7-1 |
Journal Detail:
|
Title: Journal of the American Medical Informatics Association : JAMIA Volume: - ISSN: 1527-974X ISO Abbreviation: - Publication Date: 2011 Jul |
Date Detail:
|
Created Date: 2011-7-4 Completed Date: - Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 9430800 Medline TA: J Am Med Inform Assoc Country: - |
Other Details:
|
Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
|
Human Language Technology Research Institute, University of Texas at Dallas, Richardson, Texas, USA. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
|
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Computerization of workflows, guidelines, and care pathways: a review of implementation challenges f...
Next Document: Prospective Outcomes of Injury Study: recruitment, and participant characteristics, health and disab...