We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these models from the perspective of personalized medicine. Decision-driven models have a number of benefits that are of particular value in this domain, such as being easily interpretable and naturally quantifying confidences in both evidence and predictions. We show how decision-driven models can easily be constructed using probabilistic soft logic, a recently introduced framework for statistical relational learning and inference which allows the specification of medical domain knowledge in concise first-order-logic rules with assigned confidence values.

@InProceedings{bach:pmpm10,
  author       = "Bach, Stephen H. and Broecheler, Matthias and Kok, Stanley and Getoor, Lise",
  title        = "Decision-Driven Models with Probabilistic Soft Logic",
  booktitle    = "NIPS Workshop on Predictive Models in Personalized Medicine",
  year         = "2010",
}