Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database

TL;DR

This paper introduces a novel feature matrix approach for detecting adverse drug reactions from large-scale medical data, improving detection accuracy over existing methods.

Contribution

The study proposes a new feature matrix concept and feature selection technique to automatically identify significant adverse drug reactions from big medical datasets.

Findings

Successfully detected major side effects for three drugs.

Achieved better performance than existing computerized methods.

Validated the approach on real-world medical data.

Abstract

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM) is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN) database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.