Title: Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling
Authors: Leila Yousefi (Leila.Yousefi@brunel.ac.uk), Stephen Swift, Mahir Arzoky, Lucia Saachi, Luca Chiovato and Allan Tucker
Abstract: Clinicians predict disease and related complications based on prior knowledge and each individual patient’s clinical history. The prediction process is complex because of the existence of unmeasured risk factors, the unexpected development of complications, and varying responses of patients to the disease
over time. Exploiting hidden variables (i.e., unmeasured risk factors) can improve the modeling of disease progression and being able to understand the semantics of the hidden variables will enable clinicians to focus on the early diagnosis and treatment of unexpected conditions among sufferers. However, the overuse
of hidden variables can lead to complex models that can overfit and are not well understood (being ‘black box’ in nature). Identifying and understanding groups of patients with similar disease profiles (based on discovered hidden variables) makes it possible to better understand the manner of disease progression in different patients while improving prediction. Here, we explore the use of a stepwise method for incrementally identifying hidden variables based on the Induction Causation (IC*) algorithm. We exploit Dynamic Time Warping (DTW) and hierarchical clustering to cluster patients based upon these hidden variables to begin to uncover their meaning with respect to the complications of Type 2 Diabetes Mellitus (T2DM) patients. Our results reveal that inferring a small number of targeted hidden variables and using them to cluster patients not only leads to an improvement in the prediction accuracy but also assists the explanation of different discovered sub-groups.
Conference: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2018)