title: Requirements for the formal representation of pathophysiology mechanisms by clinicians.
abstract: Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing-not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice.
authors.affiliation: Auckland Bioengineering Institute (ABI), University of Auckland, Auckland, New Zealand; Farr Institute, University College London, 222 Euston Road, London, UK.
authors.forename: B
authors.identifier: 0000-0003-0638-5274
authors.initials: B
authors.lastname: de Bono
authors.affiliation: Leiden Institute of Advanced Computer Science , University of Leiden , Leiden , The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Helvensteijn
authors.affiliation: Leiden Institute of Advanced Computer Science , University of Leiden , Leiden , The Netherlands.
authors.forename: N
authors.initials: N
authors.lastname: Kokash
authors.affiliation: Leiden Institute of Advanced Computer Science , University of Leiden , Leiden , The Netherlands.
authors.forename: I
authors.initials: I
authors.lastname: Martorelli
authors.affiliation: Auckland Bioengineering Institute (ABI) , University of Auckland , Auckland , New Zealand.
authors.forename: D
authors.initials: D
authors.lastname: Sarwar
authors.affiliation: University of East London , University Way, London , UK.
authors.forename: S
authors.initials: S
authors.lastname: Islam
authors.affiliation: Farr Institute , University College London , 222 Euston Road, London , UK.
authors.forename: P
authors.initials: P
authors.lastname: Grenon
authors.affiliation: Auckland Bioengineering Institute (ABI), University of Auckland, Auckland, New Zealand; Department of Physiology, Anatomy and Genetics (DPAG), University of Oxford, Oxford, UK.
authors.forename: P
authors.initials: P
authors.lastname: Hunter
keywords: clinical community; disease mechanism modelling; knowledge management; pathophysiology; physiome community