Ontology driven controlled natural language clinical decision support system for the cardiovascular specialty.

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Bibliographic Details
Title:	Ontology driven controlled natural language clinical decision support system for the cardiovascular specialty.
Authors:	Mendes, David¹ dmendes@uevora.pt, Rodrigues, Irene Pimenta¹, Baeta, Carlos Fernandes², Solano-Rodriguez, Carlos³
Source:	Interdisciplinarity in Engineering. 2014, Vol. 16, p1493-1501. 9p.
Subjects:	Medical care, Cardiovascular diseases, Ontology, Medical sciences, Public health, Health
Abstract:	We present an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment. We introduce our proposed ontology framework, Ontology for General Clinical Practice, that we developed enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, extending upon the OBO Foundry principles. It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). The knowledge base is automatically populated by means of a tutored acquisition from clinical reports using Controlled Natural Language rendering an ontology driven question answering system with high recall, precision and F-Measure that competes in its specific sub-domain with the more advanced current NLP systems developed in general for the healthcare domain. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	We present an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment. We introduce our proposed ontology framework, Ontology for General Clinical Practice, that we developed enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, extending upon the OBO Foundry principles. It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). The knowledge base is automatically populated by means of a tutored acquisition from clinical reports using Controlled Natural Language rendering an ontology driven question answering system with high recall, precision and F-Measure that competes in its specific sub-domain with the more advanced current NLP systems developed in general for the healthcare domain. [ABSTRACT FROM AUTHOR]
ISSN:	22850945
DOI:	10.1016/j.protcy.2014.10.170