Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT Discourse Representation Theory structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, 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.

[1]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[2]  Angus Roberts,et al.  Building a semantically annotated corpus of clinical texts , 2009, J. Biomed. Informatics.

[3]  Gerold Schneider,et al.  Discourse representation structures for ACE 5 , 2006 .

[4]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[5]  João Varajão,et al.  Risk Management Information System Architecture for a Hospital Center: The Case of CHTMAD , 2013, Int. J. Heal. Inf. Syst. Informatics.

[6]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[7]  John F. Hurdle,et al.  Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.

[8]  Markus Krötzsch,et al.  The Incredible ELK , 2013, Journal of Automated Reasoning.

[9]  Lauren B. Eder,et al.  Managing Healthcare Information Systems with Web-Enabled Technologies , 1999 .

[10]  Jerry B. Weinberg,et al.  Patient-doctor interconnectivity: improving health care management and patient compliance with Web technology , 2000 .

[11]  Gerold Schneider,et al.  Attempto Controlled English Meets the Challenges of Knowledge Representation, Reasoning, Interoperability and User Interfaces , 2006, FLAIRS.

[12]  Amal Zouaq,et al.  A Survey of Domain Ontology Engineering: Methods and Tools , 2010, Advances in Intelligent Tutoring Systems.

[13]  William R. Hogan,et al.  Natural Language Processing methods and systems for biomedical ontology learning , 2011, J. Biomed. Informatics.

[14]  Anand Kumar,et al.  Text mining and ontologies in biomedicine: Making sense of raw text , 2005, Briefings Bioinform..

[15]  Chimezie Ogbuji A Framework Ontology for Computer-Based Patient Record Systems , 2011, ICBO.

[16]  Illhoi Yoo,et al.  Data Mining in Healthcare and Biomedicine: A Survey of the Literature , 2012, Journal of Medical Systems.

[17]  F. Longo,et al.  Advanced Solutions for Healthcare Facility Management , 2014 .

[18]  B Smith,et al.  Putting Biomedical Ontologies to Work , 2010, Methods of Information in Medicine.

[19]  Jane. Fitzpatrick,et al.  Video Conferencing to Enhance the Lives of Children Living with Disabilities , 2011 .

[20]  Mohammed Bennamoun,et al.  Ontology learning from text: A look back and into the future , 2012, CSUR.

[21]  Amit P. Sheth,et al.  Semantics Driven Approach for Knowledge Acquisition From EMRs , 2014, IEEE Journal of Biomedical and Health Informatics.