Data-Driven Allocation of Preventive Care

Data-Driven Allocation of Preventive Care

Background

Healthcare is increasingly focusing on preventive treatments to improve disease progression or even prevent diseases altogether. Unfortunately, preventive treatment of large populations is not feasible due to cost constraints, which raises the question in healthcare management of which patients benefit most from treatments.

Objective

In this project, we develop a decision model based on machine learning and optimization, which finds the subgroup of patients who benefit most from treatment for a fixed budget for preventive treatments. Accordingly, the expected number of diseases is minimized.

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