Automating the identification of patient safety incident reports using multi-label classification

Ying Wang*, Enrico Coiera, William Runciman, Farah Magrabi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)
64 Downloads (Pure)

Abstract

Automated identification provides an efficient way to categorize patient safety incidents. Previous studies have focused on identifying single incident types relating to a specific patient safety problem, e.g., clinical handover. In reality, there are multiple types of incidents reflecting the breadth of patient safety problems and a single report may describe multiple problems, i.e., it can be assigned multiple type labels. This study evaluated the abilty of multi-label classification methods to identify multiple incident types in single reports. Three multi-label methods were evaluated: binary relevance, classifier chains and ensemble of classifier chains. We found that an ensemble of classifier chains was the most effective method using binary Support Vector Machines with radial basis function kernel and bag-of-words feature extraction, performing equally well on balanced and stratified datasets, (F-score: 73.7% vs. 74.7%). Classifiers were able to identify six common incident types: falls, medications, pressure injury, aggression, documentation problems and others.

Original languageEnglish
Title of host publicationMEDINFO 2017
Subtitle of host publicationPrecision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
EditorsAdi V. Gundlapalli, Marie-Christine Jaulent, Dongsheng Zhao
Place of PublicationAmsterdam
PublisherIOS Press
Pages609-613
Number of pages5
Volume245
ISBN (Electronic)9781614998303
ISBN (Print)9781614998297
DOIs
Publication statusPublished - 2017
Event16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China
Duration: 21 Aug 201725 Aug 2017

Publication series

NameStudies in Health Technology and Informatics
Volume245
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017
CountryChina
CityHangzhou
Period21/08/1725/08/17

Bibliographical note

Copyright the International Medical Informatics Association (IMIA) and IOS Press 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Machine learning
  • Patient safety
  • Risk management

Fingerprint Dive into the research topics of 'Automating the identification of patient safety incident reports using multi-label classification'. Together they form a unique fingerprint.

Cite this