  Decision-making related to health is complex. Machine learning (ML) and
patient generated data can identify patterns and insights at the individual
level, where human cognition falls short, but not all ML-generated information
is of equal utility for making health-related decisions. We develop and apply
attributable components analysis (ACA), a method inspired by optimal transport
theory, to type 2 diabetes self-monitoring data to identify patterns of
association between nutrition and blood glucose control. In comparison with
linear regression, we found that ACA offers a number of characteristics that
make it promising for use in decision support applications. For example, ACA
was able to identify non-linear relationships, was more robust to outliers, and
offered broader and more expressive uncertainty estimates. In addition, our
results highlight a tradeoff between model accuracy and interpretability, and
we discuss implications for ML-driven decision support systems.
