Using machine learning to identify top predictors for nurses’ willingness to report medication errors

Renjie Hu*, Amany Farag, Kaj-Mikael Björk, Amaury Lendasse

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper presents a novel methodology to analyze nurses’ willingness to report medication errors. Parallel Extreme Learning Machines were applied to identify the top interpersonal and organizational predictors and Self-Organizing Maps to create comprehensive visualization. The results of the data analysis were targeted to improve the likelihood of nurses reporting of medication errors. ELMs are accurate by extremely fast prediction models. Self-Organizing Maps enable us to perform non-linear dimensionality reduction to get an accurate visualization of the selected variables. Combining both techniques reduces the curse of dimensionality and improves the interpretability of the visualization.
Original languageEnglish
Article number100049
Peer-reviewed scientific journalArray
Volume8
Pages (from-to)100049
Number of pages11
ISSN2590-0056
DOIs
Publication statusPublished - 09.11.2020
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Variable selection
  • Data visualization
  • Dimensionality reduction
  • Extreme learning machines
  • Self-organizing maps
  • Medication error reporting

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