Journal article

A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding

Christopher McMaster, David Liew, Claire Keith, Parnaz Aminian, Albert Frauman

DRUG SAFETY | ADIS INT LTD | Published : 2019

Abstract

INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10th Revision (ICD-10) codes have been used in epidemiological studies of ADRs and offer the potential for automated ADR detection systems. OBJECTIVE: The aim of this study was to develop an automated ADR detection system based on ICD-10 codes, using machine-learning algorithms to improve accuracy and efficiency. METHODS: For a 12-month period from December 2016 to November 2017, every inpatient episode receiving an ICD-10 code in the range Y40.0-Y5..

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University of Melbourne Researchers