SeDAn: a Plausible Reasoning Approach for Semantics-based Data Analytics in Healthcare

Plausible Reasoning (PR) is an inferencing mechanism to derive solutions when dealing with incomplete knowledge. When developing data-driven models for clinical decision support, the completeness of the data is always a consideration. PR provides a practical approach to extend the knowledge-base of a clinical decision support system by abstracting plausible assertions from heath data. Implementation of plausible reasoning relies on fine-grained knowledge of how different concepts are semantically related. The Semantic Web provides formalisms to semantically represent knowledge at various levels of expressivity, and to reason over the knowledge to perform semantic analytics based on healthcare data. This paper proposes a SEmantics-based Data ANalytics framework (SeDan) to investigate the potential of implementing plausible reasoning using the Semantic Web technologies. In particular, we will evaluate the efficacy of the proposed framework in healthcare to perform effective semantic analytics using partial health data to make better decisions in disease diagnosis and longterm care. We demonstrate the efficacy of SeDan by answering medical queries posed by BioASQ challenges using Disease ontology, DrugBank and Semantic MEDLINE databases.

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