Text and Ontology Driven Clinical Decision Support System

Links to Files

https://ebiquity.umbc.edu/paper/html/id/651/Text-and-Ontology-Driven-Clinical-Decision-Support-System

Permanent Link

http://hdl.handle.net/11603/11846

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2013-10-31

Type of Work

conference papers and proceedings pre-print
Text

Department

Program

Citation of Original Publication

Deepal Dhariwal, Anupam Joshi, and Michael A. Grasso, Text and Ontology Driven Clinical Decision Support System, American Medical Informatics Association (AMIA) Annual Symposium, 2013, https://ebiquity.umbc.edu/paper/html/id/651/Text-and-Ontology-Driven-Clinical-Decision-Support-System

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Subjects

Text
Ontology Driven
clinical decision support
knowledge base
physicians
UMBC Ebiquity Research Group

Abstract

In this paper, we discuss our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians.