Workshop Paper, NIPS Workshop on Predictive Models in Personalized Medicine, 2010, Whistler, Canada
Authors: Stephen H. Bach, Matthias Broecheler, Stanley Kok, and Lise Getoor
Direct link to paper
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.
The paper is presented as a poster. Follow the link for more information on decision-driven modeling in probabilistic soft logic.
Where is the knowledge we have lost in information?
- T.S. Elliot, The Rock
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