Semantic Annotation and Inference for Medical Knowledge Discovery

We describe our vision for a new generation medical knowledge annotation and acquisition system called SENTIENT-MD (Semantic Annotation and Inference for Medical Knowledge Discovery). Key aspects of our vision include deep Natural Language Processing techniques to abstract the text into a more semantically meaningful representation guided by domain ontology. In particular, we introduce a notion of semantic fitness to model an optimal level of abstract representation for a text fragment given a domain ontology. We apply this notion to appropriately condense and merge nodes in semantically annotated syntactic parse trees. These transformed semantically annotated trees are more amenable to analysis and inference for abstract knowledge discovery, such as for automatically inferring general medical rules for enhancing an expert system for nuclear cardiology. This work is a part of a long term research effort on continuously mining medical literature for automatic clinical decision support.

Read the paper:

Semantic Annotation and Inference for Medical Knowledge Discovery

by Saurav Sahay, Eugene Agichtein, Baoli Li, Ernie Garcia, Ashwin Ram

NSF Symposium on Next Generation of Data Mining (NGDM-07), Baltimore, MD, October 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-16.pdf
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