Authors:
Ermelinda Oro
and
Massimo Ruffolo
Affiliation:
University of Calabria, Italy
Keyword(s):
Knowledge Representation and Reasoning, Knowledge Management, Ontology, Workflow, Data Mining, Workflow Mining, Decision Support System, Health Care Information System, Clinical Process, Medical Knowledge.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Engineering
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Knowledge-Based Systems Applications
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Software Engineering
;
Symbolic Systems
Abstract:
Managing costs and risks is an high priority theme for health care professionals and providers. A promising approach for reducing costs and risks, and enhancing patient safety, is the definition of process-oriented clinical information systems. In the area of health care information systems, a number of systems and approaches to medical knowledge and clinical processes representation and management are available. But no systems that provide integrated approaches to both declarative and procedural medical knowledge are currently available. In this work a clinical process management system aimed at supporting a semantic process-centered vision of health care practices is described. The system is founded on an ontology-based clinical knowledge representation framework that allows representing and managing, in a unified way, both medical knowledge and clinical processes. The system provides functionalities for: (i) designing clinical processes by exploiting already existing and ad-hoc me
dical ontologies and guideline base; (ii) executing clinical processes and monitoring their evolution by adopting alerting techniques that aid to prevent risks and errors; (iii) analyzing clinical processes by semantic querying and data mining techniques for making available decision support features able to contain risks and to enhance cost control and patient safety.
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