Representing Outcome-driven Higher-order Dependencies in Graphs of
  Disease Trajectories
Steven J. Krieg Nitesh V. Chawla Keith Feldman
The widespread application of machine learning techniques to biomedical data
has produced many new insights into disease progression and improving clinical
care. Inspired by the flexibility and interpretability of graphs (networks), as
well as the potency of sequence models like transformers and higher-order
networks (HONs), we propose a method that identifies combinations of risk
factors for a given outcome and accurately encodes these higher-order
relationships in a graph. Using historical data from 913,475 type 2 diabetes
(T2D) patients, we found that, compared to other approaches, the proposed
networks encode significantly more information about the progression of T2D
toward a variety of outcomes. We additionally demonstrate how structural
information from the proposed graph can be used to augment the performance of
transformer-based models on predictive tasks, especially when the data are
noisy. By increasing the order, or memory, of the graph, we show how the
proposed method illuminates key risk factors while successfully ignoring noisy
elements, which facilitates analysis that is simultaneously accurate and
interpretable.