  Predictive models are finding an increasing number of applications in many
industries. As a result, a practical means for trading-off the cost of
deploying a model versus its effectiveness is needed. Our work is motivated by
risk prediction problems in healthcare. Cost-structures in domains such as
healthcare are quite complex, posing a significant challenge to existing
approaches. We propose a novel framework for designing cost-sensitive
structured regularizers that is suitable for problems with complex cost
dependencies. We draw upon a surprising connection to boolean circuits. In
particular, we represent the problem costs as a multi-layer boolean circuit,
and then use properties of boolean circuits to define an extended feature
vector and a group regularizer that exactly captures the underlying cost
structure. The resulting regularizer may then be combined with a fidelity
function to perform model prediction, for example. For the challenging
real-world application of risk prediction for sepsis in intensive care units,
the use of our regularizer leads to models that are in harmony with the
underlying cost structure and thus provide an excellent prediction accuracy
versus cost tradeoff.
