Computational Reasoning across Multiple Models
- Affiliation of the authors: Centre for Health Informatics, University of New South Wales, Sydney, NSW, Australia
- Coorespondence: Dr. Guy Tsafnat, Centre for Health Informatics, University of New South Wales, Sydney, NSW 2052 Australia Email: <guyt{at}unsw.edu.au>
- Received 3 October 2008
- Accepted 29 June 2009
Abstract
Computational support of clinical decisions frequently requires the integration of data in a variety of formats and from multiple sources and domains. Some impressive multiscale computational models of biological phenomena have been developed as part of the study of disease and healthcare systems. One can now contemplate harnessing these models arising from computational biology and using highly interconnected clinical data to support clinical decision-making. Indeed, understanding how to build computational systems able to reason across heterogeneous models and datasets is one of the major and perhaps foundational challenges of translational biomedical informatics. In this paper, the authors examine the use of multimodels (models composed of several daughter models) and explore three major research challenges to reasoning across multiple models: model selection, model composition, and computer aided model construction.
Footnotes
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This work is supported by a New South Wales Health Capacity Building Infrastructure Grant.
