Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining

TL;DR

This paper introduces a new causal contrast set mining method to identify risk factors for drug side effects using big observational data, overcoming bias and passive data collection limitations.

Contribution

The paper presents a novel methodology for efficiently identifying causal risk factors for rare side effects in large observational datasets.

Findings

Identified known risk factors like diuretics prescription.

Highlighted increased susceptibility in high-risk patient groups.

Demonstrated effectiveness in uncovering causal relationships in big data.

Abstract

Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for causal inference is limited due to the passive way in which the data are collected resulting in various forms of bias. In this paper we investigate a method that can overcome these limitations and determine causal contrast set rules efficiently from big data. In particular, we present a new methodology for the purpose of identifying risk factors that increase a patients likelihood of experiencing the known rare side effect of renal failure after ingesting aminosalicylates. The results show that the methodology was able to identify previously researched risk factors such as being prescribed diuretics and highlighted that patients with a higher than average risk of renal failure may be even more susceptible to experiencing it as a side effect after ingesting aminosalicylates.