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. 2016 Apr 6;6(2):20150099.
doi: 10.1098/rsfs.2015.0099.

Requirements for the Formal Representation of Pathophysiology Mechanisms by Clinicians

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Free PMC article

Requirements for the Formal Representation of Pathophysiology Mechanisms by Clinicians

B de Bono et al. Interface Focus. .
Free PMC article

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.

Keywords: clinical community; disease mechanism modelling; knowledge management; pathophysiology; physiome community.

Figures

Figure 1.
Figure 1.
An example of a connected dependency series of six variables (rounded boxes) extracted from the Guyton 1992 model, linked via five equations (circles). Independent variables are linked to equation nodes via input arrows, dependent variables via output arrows (therefore, PRA is both an independent variable to Eq2 and a dependent variable to Eq1). Each equation may involve more than one independent variable (hence the dotted vertical arrows). The free-text definition associated with each variable symbol has been copied verbatim from the available documentation of the original model. (Online version in colour.)
Figure 2.
Figure 2.
A sketch depicting cardiovascular and neural anatomical routes between functionally related compartments involved in conveying the interaction between measurements A, B and C as well as model variables VRE, PRA, ANPR2 and AAR (shown in pink, see also table 1). Here, compartments are represented in the shape of either boxes (e.g. adrenal medulla) or connecting lines (e.g. vagus nerve). The drawing of symmetrically duplicated compartments (e.g. right and left kidneys) was avoided in this diagram to reduce complexity of illustration. The following references were used in building this circuit: [–24]. (Online version in colour.)
Figure 3.
Figure 3.
A transfer graph for the hydronephrosis pathophysiology scenario. The nodes in the graph consist of located measurements. The main types of measurement are Process rate (P) or Material state (M). The type of biophysical (e.g. pressure, mass) measurement is depicted in red in the top right corner of the node symbol. In the bottom right, the location of the measurement is indicated geometrically as conduit Wall (W) or Content (C), as well as in more detail in free text (red). The direction of the edges (green) indicates the location with respect to the equals sign in equations I–VI, from r.h.s. to l.h.s. The subgraphs highlighted by regions A and B are discussed in the text. A mapping between the above nodes and the variables in equations I–VI is provided in figure 4. (Online version in colour.)
Figure 4.
Figure 4.
A mapping between the above nodes and the variables in equations I–VI. (Online version in colour.)

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