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 our study we present a design for a decision support system for patients suffering from Bipolar Disorder (BD). Bipolar Disorder is a recurrent and highly disabling psychiatric illness that evolves constantly in time and often leads to crucial incidents. We focus on Bipolar Depression and especially on a Breakthrough Depressive Episode scenario that occurs when a patient shows depressive symptoms during pharmaceutical treatment. Using Semantic Web Technologies we developed SybillaTUC, a prototype Clinical Decision Support System which combines the clinical guidelines for Bipolar Disorder with a patient's condition and his medical record. The system is able to predict the evolution of the disease for each patient, alerting the clinician on the possibility of a crucial incident suggesting optimal treatment.
    2014 5th International Conference on Information, Intelligence, Systems and Applications (IISA); 07/2014
  • 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.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Clinical Decision Support Systems (CDSSs) play important roles aiding in patient care; they provide accurate data analysis and timely evidence-informed recommendations. Although the availability of biomedical data continues to flourish, there have been limited translations of this type of data to information in real-time at the bedside. Existing systems have either focused on providing process-oriented or knowledge-modeled frameworks, often relying on retrospective data analysis. We have developed a framework capable of providing clinicians the ability to represent existing knowledge and processes in realtime. This framework presents a real-time environment for modeling clinical workflow processes abstracted from clinical guidelines, while applying existing knowledge to produce intelligent evidence-informed recommendations. In this paper we provide a framework to support the detection of neonatal hypoglycaemia using a design supporting the automated realtime, evidence-informed enactment of complex businessprocesses existing in clinical practice guidelines.
    IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2012); 01/2012

Full-text (2 Sources)

Available from
Jun 2, 2014