Plausible reasoning reflects the "plasticity" element of human reasoning, which, by leveraging the semantics of relevant concepts, allows dealing with incomplete data during decision making. We propose the SEmantics-based Data ANalytics (SeDan) framework that integrates plausible reasoning with expressive, fine-grained biomedical ontologies. Using this framework, an unresolvable query can be rewritten to explore the semantic knowledge graph and infer new knowledge. While the gained insights may be plausible, i.e., not supported by crisp deductive reasoning, they may still aid complex medical decision making by recommending plausible solutions. In this paper, we investigate the efficiency of SeDan in a real-world medical setting to pose intelligent medical queries from BioASQ challenges over the MEDLINE database. Experimental results show that SeDan can expand the query answering coverage by resolving up to 45% of initially unresolvable queries. The correctness of the inferred answers, as well as the underlying plausible reasoning processes, was verified by a domain expert.

Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially web ontology language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL-based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning-based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + semantic web rule language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.

Semantic Web technologies have shown to have great potential in many different domains, to facilitate knowledge representation, exchange and reasoning, in a formal and yet both human and machine understandable way. In particular, within the health domain, they enable knowledge integration and understanding by explicitly defining and linking concepts and relationships using ontologies to information within clinical knowledge bases. This additional metadata also allows for automated decision support and semantic based analytics to be implemented, that facilitate improved healthcare at a lower cost. Unfortunately many existing datasets in healthcare environments are still stored in relational databases, as opposed to using semantic technologies. Due to this, the link with explicit metadata is often lacking or non-existent. Furthermore, both the databases and the clinical terminologies can be considerably large, making the mapping and subsequent uses of the information a difficult process. In a full fledged decision support system the level and accuracy of the mapping can greatly influence the effectiveness of any subsequent analysis and decision support tasks. This is especially true in clinical scenarios, where very large and complex sets of terms need to be mapped to relational databases. In this paper we aim to provide a general approach for interlinking relational data with clinical ontology based metadata that allows for a fine grade evaluation, with respect to the mapping's impact on analytics. We evaluate our approach by mapping information from clinical terminologies, such as SNOMED CT, to a large laboratory dataset contained in a relational database, with the goal of creating a full fledged, semantically enabled, analytics and decision support system.

In this paper we pursue the standardization of care, for a specific medical condition, across multiple institutions through the alignment of institutionspecific clinical pathways. Our approach is to represent institution-specific Clinical Pathways (CP) using an ontological model and then align the common care activities between multiple CP, based on their semantic descriptions, to generate a `maximally standardized' care model that merges common care activities whilst allowing to administer unique institution-specific care activities. Our semantic web based approach is used to develop a standardized prostate cancer care model for three Canadian institutions, such that the standardized model can be executed via a web-based system to streamline patient care.

We present an E-Research platform for studying the impact of changes in the ecosystem on oceans and marine life. We have developed the Platform for Ocean Knowledge Management (POKM) that offers a suite of services for researchers to (a) access, share, integrate and operationalize the data, models and knowledge resources available at multiple sites; (b) collaborate in joint scientific research experiments by sharing resources, results, expertise and models; and (c) form a virtual community of researchers. We take a knowledge management approach to establish conceptual, terminological and data level interoperability between ocean and marine life science communities so that they can collaborate to conduct complex experiments. POKM is supported by the CANARIE high bandwidth network enables rapid sharing of high-volume data from distributed repositories to users across the world.

Breast cancer is the most common cancer among women in Canada. There are identified incentives to the transfer of breast cancer follow-up care to family physicians after primary treatment has been completed by oncologists at the tertiary care centers. This paper presents a semantic web approach to develop a clinical decision support system to support family physicians to provide breast cancer follow-up care. Our approach involved the computerization and execution of a breast cancer follow-up clinical practice guideline. The computerization of the clinical practice guideline led to the development of a breast cancer ontology. We present our breast cancer ontology which models the knowledge inherent within the breast cancer follow-up clinical practice guideline - the breast cancer ontology serves as the knowledge source to determine patient-specific recommendations. In this paper, we discuss the ontology engineering process that highlights the specification of our breast cancer ontology in terms of clinical concepts and the relationships between the concepts expressed as OWL classes and properties, using the protege ontology development tool.

Clinical evidence exists in modalities other than published clinical literature, such as: clinical data ranging from patient clinical profiles to clinical trials; clinical experiences of eminent medical practitioners; and medical knowledge bases encapsulating knowledge about patient care, healthcare guidelines and protocols, clinical workflow, and so on. We propose a technology-enriched strategy to exploit advanced computer technologies-knowledge management, data mining, case-based reasoning strategies and Internet technology-within traditional evidence-based medicine systems to derive all-encompassing clinical evidence derived from heterogeneous clinical evidence modalities. The paper features a conceptual overview of an integrated clinical evidence system designed to augment the typical literature-based clinical evidence with additional technology-mediated clinical evidence.

Provides a technology-enriched solution to Web-mediated patient empowerment initiatives via the implementation of an intelligent information structure that features (1) the dynamic composition of personalized healthcare information (PHI) conforming to an individual's EMR (electronic medical record) based health profile; and (2) proactive Internet-based delivery of PHI. Healthcare information personalization is achieved via profile-specific selection and template-specific aggregation of multiple, fine-grained, pre-authored, generic, topic-specific healthcare information content. The use of intelligent constraint satisfaction techniques ensure the medical correctness of the dynamically composed PHI document. The application of Internet-based push technology allows the PHI document to be pushed to the individual's e-mail account, thus guaranteeing the timely availability of high-quality health maintenance information.