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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Automatically extracting clinically useful sentences from UpToDate to support clinicians' information needs
Rashmi Mishra1, Guilherme Del Fiol1, Halil Kilicoglu2
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 20, 2014
Summary
Algorithms using semantic predications can identify clinically useful sentences in UpToDate, reducing clinician search time. This approach aids in efficient patient care decision-making by pinpointing vital information.
Area of Science:
- Medical Informatics
- Natural Language Processing
Background:
- Clinicians require timely access to information for patient care.
- Online health resources like UpToDate are valuable but can be time-consuming to search.
Purpose of the Study:
- To develop and evaluate algorithms for extracting clinically relevant sentences from UpToDate.
- To improve the efficiency of accessing critical patient care information.
Main Methods:
- Developed algorithms utilizing semantic predications from SemRep, a natural language processing tool.
- Compared two algorithms against a gold standard of clinician-rated useful UpToDate sentences.
Main Results:
- A strong correlation (0.95) was found between clinically useful sentences and predication frequency.
- The developed algorithms showed similar performance in identifying top relevant sentences (precision 53% vs. 49%).
Conclusions:
- Semantic predications show promise for extracting clinically useful information from medical resources.
- Further research is necessary to refine these algorithms for optimal clinical application.
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