  Diabetes is a major public health problem in the United States, affecting
roughly 30 million people. Diabetes complications, along with the mental health
comorbidities that often co-occur with them, are major drivers of high
healthcare costs, poor outcomes, and reduced treatment adherence in diabetes.
Here, we evaluate in a large state-wide population whether we can use
artificial intelligence (AI) techniques to identify clusters of patient
trajectories within the broader diabetes population in order to create
cost-effective, narrowly-focused case management intervention strategies to
reduce development of complications. This approach combined data from: 1)
claims, 2) case management notes, and 3) social determinants of health from
~300,000 real patients between 2014 and 2016. We categorized complications as
five types: Cardiovascular, Neuropathy, Opthalmic, Renal, and Other. Modeling
was performed combining a variety of machine learning algorithms, including
supervised classification, unsupervised clustering, natural language processing
of unstructured care notes, and feature engineering. The results showed that we
can predict development of diabetes complications roughly 83.5% of the time
using claims data or social determinants of health data. They also showed we
can reveal meaningful clusters in the patient population related to
complications and mental health that can be used to cost-effective screening
program, reducing the number of patients to be screened down by 85%. This study
outlines creation of an AI framework to develop protocols to better address
mental health comorbidities that lead to complications development in the
diabetes population. Future work is described that outlines potential lines of
research and the need for better addressing the 'people side' of the equation.
