Levels of Knowledge Certainty for Diagnoses in Swedish Clinical Records
Sumithra Velupillai (Stockholm University)
NICTA SEMINARDATE: 2011-10-04
TIME: 16:00:00 - 17:00:00
LOCATION: NICTA - 7 London Circuit
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ABSTRACT:
Different levels of knowledge certainty, or factuality levels, are expressed in clinical health record documentation. This information is currently not fully exploited, as the subtleties expressed in natural language cannot easily be machine analyzed. Extracting relevant information from knowledge-intensive resources such as electronic health records can be used for improving health care in general by e.g. building automated information access systems. We present an annotation model of six factuality levels linked to diagnoses in Swedish clinical assessments from an emergency ward. Our main findings are that overall agreement is fairly high (0.7/0.58 F-measure, 0.73/0.6 Cohenas I, Intra/Inter). These distinctions are important for knowledge models, since only approx. 50% of the diagnoses are affirmed with certainty. The resulting corpus has been used for training and evaluating and automatic classifier using Conditional Random Fields and local context features. Preliminary results are promising (0.699 F-measure), merging intermediate annotation classes improves results further (0.762 F-measure).
BIO:
Velupillai is a PhD student at the Department of Computer and Systems Sciences at Stockholm University since April 2007. She successfully defended her Licentiate Thesis Swedish Health Data a" Information Access and Representation on the 6th of October, 2009. Velupillai is also affiliated with the Swedish National Graduate School of Language Technology (GSLT), has participated in several research projects, and is currently part of the Nordic research network HEXAnord. Velupillai has a background in Computational Linguistics and specializes in research covering both Language Technology, Information Access and Health Informatics. Velupillai has published and presented eighteen articles in renowned international conferences and journals. She is currently visiting NICTA in a joint collaboration effort with Dr. Hanna Suominen and Dr. David Martinez. This project is related to the NICTA research discipline of machine learning and funded by the NICTA business area of eHealth.
