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Interpreting Medical Tables as Linked Data to Generate Meta-Analysis Reports

Authors: Varish Mulwad, Tim Finin, and Anupam Joshi

Book Title: Proceedings of the 15th IEEE International Conference on Information Reuse and Integration

Date: August 13, 2014


Evidence-based medicine is the application of current medical evidence to patient care and typically uses quantitative data from research studies. It is increasingly driven by data on the efficacy of drug dosages and the correlations between various medical factors that are assembled and integrated through meta--analyses (i.e., systematic reviews) of data in tables from publications and clinical trial studies. We describe a important component of a system to automatically produce evidence reports that performs two key functions: (i) understanding the meaning of data in medical tables and (ii) identifying and retrieving relevant tables given a input query. We present modifications to our existing framework for inferring the semantics of tables and an ontology developed to model and represent medical tables in RDF. Representing medical tables as RDF makes it easier for the automatic extraction, integration and reuse of data from multiple studies, which is essential for generating meta--analyses reports. We show how relevant tables can be identified by querying over their RDF representations and describe two evaluation experiments: one on mapping medical tables to linked data and another on identifying tables relevant to a retrieval query.

Type: InProceedings

Publisher: IEEE Computer Society

Pages: 677 - 686

Tags: linked data, semantic web, learning, evidence based medicine

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Related Projects:

Past Projects

 MTLD: Interpreting Medical Tables as Linked Data.
 Tables to Linked Data.