Abstract

Abstract

Abstract
Adverse drug events are a leading cause of danger and cost in health care. We could reduce both the danger and the cost if we had accurate models to predict, at prescription time for each drug, which patients are most at risk for known adverse reactions to that drug, such as myocardial infarction (MI, or "heart attack") if given a Cox2 inhibitor, angioedema if given an ACE inhibitor, or bleeding if given an anticoagulant such as Warfarin. We address this task for the specific case of Cox2 inhibitors, a type of non-steroidal anti-inflammatory drug (NSAID) or pain reliever that is easier on the gastrointestinal system than most NSAIDS. Because of the MI adverse drug reaction, some but not all very effective Cox2 inhibitors were removed from the market. Specifically, we use machine learning to predict which patients on a Cox2 inhibitor would suffer an MI. An important issue for machine learning is that we do not know which of these patients might have suffered an MI even without the drug. To begin to make some headway on this important problem, we compare our predictive model for MI for patients on Cox2 inhibitors against a more general model for predicting MI among a broader population not on Cox2 inhibitors. Copyright

Abstract

Abstract

Abstract
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP2 for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.