Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases

TL;DR

This study evaluates four recent algorithms for mining longitudinal observational databases to detect adverse drug reactions, finding none consistently outperform others in identifying known side effects.

Contribution

It provides a comparative analysis of four algorithms applied to real-world data, highlighting their limitations in reliably detecting adverse drug reactions.

Findings

None of the algorithms consistently identified known adverse reactions.

All algorithms showed limitations in distinguishing true side effects from related events.

No single algorithm was found to be superior in the tested scenarios.

Abstract

Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior.