Design and application of a generic clinical decision support system for multiscale data

Abstract

Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patient's disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimer's disease.

Fig. 1.

Probability density functions of C_i and P_i, the resulting fitness (with examples at test outcome values a and b), and the optimal classification threshold $x_i^{\ast }$.

Fig. 2.

DSI tree visualizations for two patients, one healthy, one with AD. Larger node sizes indicate higher relevance (i.e., better discrimination of training classes), with irrelevant features omitted. Shades of red indicate similarity of the patient data to the disease population, shades of blue similarity to healthy.

Fig. 3.

Tiers, layers, and components of the generic decision support library, also showing the main direction of data flow.