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Publications

Publications by Vítor Santos Costa

2013

Score As You Lift (SAYL): A statistical relational learning approach to uplift modeling

Authors
Nassif, H; Kuusisto, F; Burnside, ES; Page, D; Shavlik, J; Santos Costa, V;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones. Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting. Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift. © 2013 Springer-Verlag.

2014

Late Breaking Papers of the 23rd International Conference on Inductive Logic Programming, Rio de Janeiro, Brazil, August 28th - to - 30th, 2013

Authors
Zaverucha, G; Costa, VS; Paes, AM;

Publication
ILP (Late Breaking Papers)

Abstract

2015

Guest editors' introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning

Authors
Zaverucha, G; Costa, VS;

Publication
MACHINE LEARNING

Abstract

2013

A preliminary investigation into predictive models for adverse drug events

Authors
Davis, J; Costa, VS; Peissig, P; Caldwell, M; Page, D;

Publication
AAAI Workshop - Technical Report

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

2014

Preface

Authors
Silva, F; Dutra, I; Costa, VS;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2014

Inductive Logic Programming - 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers

Authors
Zaverucha, G; Costa, VS; Paes, A;

Publication
ILP

Abstract

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