Abstract
The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed in the broader population that were not identified during clinical trials. Therefore, there is a need for continued, post-marketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as a reverse machine learning task, related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events. Copyright

Abstract
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connections between related medications or diseases. One way to capture the latent structure is via a relational clustering of objects. We propose a novel approach that, instead of pre-clustering the objects, performs a demand-driven clustering during learning. We evaluate our algorithm on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication. We find that our approach is more accurate than performing no clustering, pre-clustering, and using expert-constructed medical heterarchies. Copyright 2012 by the author(s)/owner(s).

Abstract
We are proud to introduce this special issue of LIPIcs - Leibniz International Proceedings in Informatics, dedicated to the technical communications accepted for the 28th International Conference on Logic Programming (ICLP). © Agostino Dovier and Vítor Santos Costa.

Abstract
The Dataflow execution model has been shown to be a good way of exploiting TLP, making parallel programming easier. In this model, tasks must be mapped to processing elements (PEs) considering the trade-off between communication and parallelism. Previous work on scheduling dependency graphs have mostly focused on directed a cyclic graphs, which are not suitable for dataflow (loops in the code become cycles in the graph). Thus, we present the SCC-Map: a novel static mapping algorithm that considers the importance of cycles during the mapping process. To validate our approach, we ran a set of benchmarks in on our dataflow simulator varying the communication latency, the number of PEs in the system and the placement algorithm. Our results show that the benchmark programs run significantly faster when mapped with SCC-Map. Moreover, we observed that SCC-Map is more effective than the other mapping algorithms when communication latency is higher. © 2012 IEEE.

Abstract
A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women. © 2012 Springer-Verlag.

Abstract
"Traditional" clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are "similar" among them and are "dissimilar" to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation. We present a novel conceptual clustering system for multi-relational data, based on the popular k?-?medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster's members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters. © 2012 Springer-Verlag Berlin Heidelberg.