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Knowledge Representation for Health-Care. Data, Processes and Guidelines [electronic resource] : AIME 2009 Workshop KR4HC 2009, Verona, Italy, July 19, 2009, Revised Selected Papers / edited by David Riano, Annette ten Teije, Silvia Miksch, Mor Peleg.

Contributor(s): Riano, David [editor.] | ten Teije, Annette [editor.] | Miksch, Silvia [editor.] | Peleg, Mor [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 5943Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010Edition: 1st ed. 2010.Description: 195 p. 60 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642118081.Subject(s): Data mining | Information storage and retrieval systems | Application software | Database management | Electronic data processing -- Management | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Computer and Information Systems Applications | Database Management | IT OperationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
From Patient Data to Medical Ontologies -- Creating Topic Hierarchies for Large Medical Libraries -- Bridging an Asbru Protocol to an Existing Electronic Patient Record -- From Natural Language Descriptions in Clinical Guidelines to Relationships in an Ontology -- A Hybrid Methodology for Consumer-Oriented Healthcare Knowledge Acquisition -- Identifying Disease-Centric Subdomains in Very Large Medical Ontologies: A Case-Study on Breast Cancer Concepts in SNOMED CT. Or: Finding 2500 Out of 300.000 -- Sharable Appropriateness Criteria in GLIF3 Using Standards and the Knowledge-Data Ontology Mapper -- Guideline Modeling and Tools -- Analysis of the GLARE and GPROVE Approaches to Clinical Guidelines -- Semantic Web-Based Modeling of Clinical Pathways Using the UML Activity Diagrams and OWL-S -- Extracting Qualitative Knowledge from Medical Guidelines for Clinical Decision-Support Systems -- Experiences in the Development of Electronic Care Plans for the Management of Comorbidities -- Challenges in Delivering Decision Support Systems: The MATE Experience -- Technical Solutions for Integrating Clinical Practice Guidelines with Electronic Patient Records -- Advanced Topics -- Towards a Possibility-Theoretic Approach to Uncertainty in Medical Data Interpretation for Text Generation -- Argumentation about Treatment Efficacy -- A Knowledge-Management Architecture to Integrate and to Share Medical and Clinical Data, Information, and Knowledge.
In: Springer Nature eBook
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From Patient Data to Medical Ontologies -- Creating Topic Hierarchies for Large Medical Libraries -- Bridging an Asbru Protocol to an Existing Electronic Patient Record -- From Natural Language Descriptions in Clinical Guidelines to Relationships in an Ontology -- A Hybrid Methodology for Consumer-Oriented Healthcare Knowledge Acquisition -- Identifying Disease-Centric Subdomains in Very Large Medical Ontologies: A Case-Study on Breast Cancer Concepts in SNOMED CT. Or: Finding 2500 Out of 300.000 -- Sharable Appropriateness Criteria in GLIF3 Using Standards and the Knowledge-Data Ontology Mapper -- Guideline Modeling and Tools -- Analysis of the GLARE and GPROVE Approaches to Clinical Guidelines -- Semantic Web-Based Modeling of Clinical Pathways Using the UML Activity Diagrams and OWL-S -- Extracting Qualitative Knowledge from Medical Guidelines for Clinical Decision-Support Systems -- Experiences in the Development of Electronic Care Plans for the Management of Comorbidities -- Challenges in Delivering Decision Support Systems: The MATE Experience -- Technical Solutions for Integrating Clinical Practice Guidelines with Electronic Patient Records -- Advanced Topics -- Towards a Possibility-Theoretic Approach to Uncertainty in Medical Data Interpretation for Text Generation -- Argumentation about Treatment Efficacy -- A Knowledge-Management Architecture to Integrate and to Share Medical and Clinical Data, Information, and Knowledge.

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