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Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

Ying WangEnrico CoieraWilliam RuncimanFarah Magrabi
Published in: BMC medical informatics and decision making (2017)
Binary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.
Keyphrases
  • patient safety
  • machine learning
  • quality improvement
  • deep learning
  • cardiovascular disease
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  • emergency department
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  • electronic health record