Abstract: Comparative effectiveness research for patients with multiple chronic conditions (MCC) has mainly focused on one disease at a time, or on a small number of high prevalent co-occurring two-way or three-way combinations of chronic conditions. This study uses the hierarchical clustering and association rule mining techniques to discover clinically meaningful homogenous clusters of MCC and interesting patterns of diseases associations within each subgroup from a large observational data.

Learning Objective 1: After participating in this session, the learner should be better able to:

1) Understand multiple chronic conditions (MCC) and strategies to manage complex MCC

2) Learn how to apply hierarchical clustering and association rule mining to uncover underlying structure and patterns in large data sets.


Che Ngufor (Presenter)
Mayo Clinic

Rozalina McCoy, Mayo Clinic
Lixia Yao, Mayo Clinic
Lindsey R. Sangaralingham, Mayo Clinic
Shannon M. Dunlay, Mayo Clinic
Nilay D. Shah, Mayo Clinic

Presentation Materials: