A PSO/ACO approach to knowledge discovery in a pharmacovigilance context

ABSTRACT

We propose and evaluate the use of a Particle Swarm Optimization/Ant Colony Optimization (PSO/ACO) methodology for classification and rule discovery in the context of medication postmarketing surveillance or pharmacovigilance. Our study considers a large data set of diabetic patients on two widely used antidiabetic drugs (rosiglitazone and pioglitazone), and the risk of myocardial infarction as an adverse effect. The goal is to determine the presence of previously undetected causal relationships between therapeutics, patient characteristics, and adverse medication outcomes. Since the proposed approach is able to discover classification rules, the elicited knowledge may suggest new hypotheses regarding associations between risk factors and an adverse event. Our classification results show high accuracy. Furthermore, several medication-related rules were discovered and analyzed. The elicited rules support previous studies from the medical literature. Moreover, one of the studied antidiabetic drugs (rosiglitazone) was found to have a significant higher risk of an adverse event on diabetic, hypertensive patients, as compared to the other drug. This last finding suggests that pioglitazone may have a protective effect against myocardial infarction on diabetic, hypertensive patients.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
ant algorithms., genetic based machine learning, healthcare, knowledge discovery, pharmacovigilance, postmarketing surveillance, pso/aco, swarm intelligence