Clustering Theory and Data-Driven Health Care Strategies
Technical Report,01 Sep 2014,24 Mar 2016
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States
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DoD health care requires reform with growing costs causing concerns of decreased military capability. One proposed radical strategy to fix current health care delivery systems is to organize medical teams around patients with similar treatment requirements. This is a clustering problem how do you partition the set of patients so that each group has similar treatment needs We provide advances in clustering theory relevant to this new health care strategy. In particular, we create fast certifiably optimal k-means clustering using what is known as Probably Certifiably Correct PCC algorithms which achieves state-of-the-art performance under certain models. Inspired by the health care clustering problem, we pay particular attention to a Bipartite Stochastic Block Model and produce an alternative PCC algorithm specific to this model. We conclude by demonstrating the potential utility of applying these clustering methods in health care. Using conditional entropy as a metric, clusters obtained from our methods vastly outperform partitions prescribed by subject matter experts.
- Numerical Mathematics
- Statistics and Probability
- Medicine and Medical Research