Conference Paper

Ontology Driven CPG Authoring and Execution via a Semantic Web Framework.

DOI: 10.1109/HICSS.2007.408 Conference: 40th Hawaii International International Conference on Systems Science (HICSS-40 2007), CD-ROM / Abstracts Proceedings, 3-6 January 2007, Waikoloa, Big Island, HI, USA
Source: DBLP

ABSTRACT Clinical Practice Guidelines (CPG) are used by healthcare practitioners to standardize clinical practic e and to provide evidence mediated health-care. Currently, ther e have been considerable efforts to computerize CPG so as to operationalize them within Clinical Decision Support Systems (CDSS) and to deploy them at the point of care. In our work, we take a semantic web approach - employing a domain ontology, a patient ontology, decision rules and a rule execution engine - towards the computerization and execution of CPG for CDSS. We present an ontology-driven approach for computerizing CPG and executing them based on individual patient instances. In our work we extend the Guideline Element Model (GEM) for computerizing CPG. We have (i) defined a CPG ontology based on the Document Type Definition (DTD) of GEM for ontologically representing a GEM encoded CPG; (ii) developed CPG decision logic definition tool and defined CPG rule syntax that allows practitioners to abstract and define decision logic rules based on the CPGs decision-variables inherent within the CPG; (iii) developed a forward-chaining CPG execution engine that executes the set of CPG execution logic rules using the JENA reasoning system; and (iv) implemented an automated justification tree generation module that provides the inference trace for the solution in order to assist practitioners in understanding the rationa le for the proposed recommendations. In practice, given a patient instance our CDSS is able to derive CPG based clinical recommendations. We will present a working prototype of our CPG-based CDSS for the EU Radiation Protection 118 Referral Guideline for Imaging (RPG).

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present our framework that objectively generates query from computerized clinical practice guidelines (CPGs), and determines its query type. It submits the dasiagenerated querypsila to PubMed using dasiaweb servicespsila to dasiaretrieve and linkpsila relevant medical literature pertaining to dasiacomputerized CPGs contentpsila. The framework makes use of contexts, semantics, statistical information and meta-info of the medical phrases in computerized CPG content (Extended-Knowledge Components). The medical literature dasiaretrieved and linkedpsila, by our framework, is semantically and contextually correlated to the dasiacomputerized CPGpsila knowledge content.
    Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the; 09/2009
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Providing patient-centric health care services is the goal of health-care institutions. However, due to human-related aspects, this goal is frequently undermined. PINATA offers an automated patient-centric system based upon Pervasive Ambience Intelligence techniques and enriched with Semantic Web technologies. The system makes use of RFID sensors to track the movements of patients and medical staff in order to direct staff effectively. An automated camera system monitors the patients and alerts hospital staff in case of emergencies. Through handheld devices hospital staff is automatically provided with relevant patient information gathered from various sources. PINATA is based on a Service Oriented Architecture and makes use of domain specific ontologies.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A framework has been developed that automatically generates context-specifi c query, and determines its query type, from computerized clinical practice guidelines (CPGs). The generated query can be submitted to PubMed using web services to retrieve and link relevant medical literature pertaining to the computerized CPGs content. This framework makes use of contexts, semantics, statistical information and meta-information of the medical phrases in the Extended-Knowledge Components of the computerized CPG content. The medical literature retrieved and linked, by our framework, is found to be relevant to the knowledge of the computerized CPG.

Full-text (2 Sources)

Available from
Jun 2, 2014