1. MedTime: A temporal information extraction system for clinical narratives
- Author
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Yu-Kai Lin, Hsinchun Chen, and Randall A. Brown
- Subjects
i2b2 ,Normalization (statistics) ,020205 medical informatics ,Computer science ,Health Informatics ,02 engineering and technology ,Temporal expressions ,Event recognition ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,Electronic Health Records ,Humans ,Narrative ,030212 general & internal medicine ,Temporal information ,Natural Language Processing ,Narration ,Clinical events ,Event (computing) ,business.industry ,Pattern recognition ,Computer Science Applications ,Temporal expression recognition and normalization ,Pattern recognition (psychology) ,Temporal information extraction ,Artificial intelligence ,business ,Algorithms ,Medical Informatics - Abstract
Temporal information extraction from clinical narratives is of critical importance to many clinical applications. We participated in the EVENT/TIMEX3 track of the 2012 i2b2 clinical temporal relations challenge, and presented our temporal information extraction system, MedTime. MedTime comprises a cascade of rule-based and machine-learning pattern recognition procedures. It achieved a micro-averaged f-measure of 0.88 in both the recognitions of clinical events and temporal expressions. We proposed and evaluated three time normalization strategies to normalize relative time expressions in clinical texts. The accuracy was 0.68 in normalizing temporal expressions of dates, times, durations, and frequencies. This study demonstrates and evaluates the integration of rule-based and machine-learning-based approaches for high performance temporal information extraction from clinical narratives.
- Published
- 2013
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