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Foundations of Biomedical Knowledge Representation pp 107–136Cite as

Modelling and Monitoring the Individual Patient in Real Time

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9521))

Abstract

This paper presents a framework for representing background knowledge and new data, and reasoning efficiently with this powerful combination of both knowledge and data. Domain knowledge is needed to positively bias the operation of data mining algorithms. Knowledge in the form of mathematical models can be considered sufficient statistics of all prior experimentation in the domain, embodying generic or abstract knowledge of it. We present a framework for using this knowledge in a probabilistic framework for data mining, inference, and decision making under uncertainty. Real-time data-streams, which typically contain uncertainty, are then exploited in a principled manner to individualise patient care. By combining the knowledge available in existing data streams with the expert knowledge available and an efficient inference method, we provide a powerful foundation for reasoning with uncertain and sparse data in the medical domain.

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Acknowledgments

This material is based upon works supported by the Science Foundation Ireland under Grant No. 08/RFP/CMS1254.

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Authors and Affiliations

  1. National University of Ireland, Galway, Ireland

    Catherine G. Enright & Michael G. Madden

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  1. Catherine G. Enright
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  2. Michael G. Madden
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Correspondence to Michael G. Madden .

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Editors and Affiliations

  1. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, The Netherlands

    Arjen Hommersom

  2. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, Gelderland, The Netherlands

    Peter J.F. Lucas

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Enright, C.G., Madden, M.G. (2015). Modelling and Monitoring the Individual Patient in Real Time. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-28007-3_7

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