Title: Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data,
Abstract: Objective: To evaluate unsupervised clustering methods for identifying
individual-level behavioral-clinical phenotypes that relate personal biomarkers
and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials
and Methods: We used hierarchical clustering (HC) to identify groups of meals
with similar nutrition and glycemic impact for 6 individuals with T2DM who
collected self-monitoring data. We evaluated clusters on: 1) correspondence to
gold standards generated by certified diabetes educators (CDEs) for 3
participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability
to identify patterns for another 3 participants. Results: Gold standard (GS)
included 9 patterns across 3 participants. Of these, all 9 were re-discovered
using HC: 4 GS patterns were consistent with patterns identified by HC (over
50% of meals in a cluster followed the pattern); another 5 were included as
sub-groups in broader clusers. 50% (9/18) of clusters were rated over 3 on
5-point Likert scale for validity, significance, and being actionable. After
reviewing clusters, CDEs identified patterns that were more consistent with
data (70% reduction in contradictions between patterns and participants'
records). Discussion: Hierarchical clustering of blood glucose and
macronutrient consumption appears suitable for discovering behavioral-clinical
phenotypes in T2DM. Most clusters corresponded to gold standard and were rated
positively by CDEs for face validity. Cluster visualizations helped CDEs
identify more robust patterns in nutrition and glycemic impact, creating new
possibilities for visual analytic solutions. Conclusion: Machine learning
methods can use diabetes self-monitoring data to create personalized
behavioral-clinical phenotypes, which may prove useful for delivering
personalized medicine.