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

Journal: Drug safety

Published Date: Jun 1, 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.

Authors

Christopher McMaster

Department of Clinical Pharmacology, Austin Health, Level 5, Lance Townsend Building, Studley Rd, Heidelberg, VIC, 3084, Australia. christopher.mcmaster@austin.org.au.
David Liew

Department of Clinical Pharmacology, Austin Health, Level 5, Lance Townsend Building, Studley Rd, Heidelberg, VIC, 3084, Australia.
Claire Keith

Department of Pharmacy, Austin Health, Heidelberg, VIC, Australia.
Parnaz Aminian

Department of Pharmacy, Austin Health, Heidelberg, VIC, Australia.
Albert Frauman

Department of Clinical Pharmacology, Austin Health, Level 5, Lance Townsend Building, Studley Rd, Heidelberg, VIC, 3084, Australia.

Keywords

Adverse Drug Reaction Reporting Systems Algorithms Clinical Coding Drug-Related Side Effects and Adverse Reactions Hospitalization Hospitals Humans International Classification of Diseases Machine Learning ROC Curve

External Resources

View on PubMed Access via DOI PubMed (30725336)

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

Abstract

Authors

Keywords

External Resources

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