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Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest

Krzysztof Gajowniczek1, Iga Grzegorczyk2, Tomasz Ząbkowski3

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This study reduces false clinical alarms in intensive care units using random forest methods. The research developed a novel synthetic measure to improve arrhythmia detection accuracy, showing promising results for critical care patient safety.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Clinical Informatics

Background:

  • High rates of false clinical alarms in intensive care units (ICUs) cause patient stress and alarm fatigue in staff.
  • Alarm fatigue can lead to critical true alarms being missed, posing a significant risk to patient safety.
  • Existing methods struggle with the complexity of accurately classifying multiple arrhythmia types.

Purpose of the Study:

  • To develop and evaluate a random forest-based method for reducing false arrhythmia alarms in ICUs.
  • To explore different probability and class assignment strategies to enhance ensemble classifier accuracy.
  • To introduce a novel synthetic measure for optimizing classification performance based on rank aggregation.

Main Methods:

  • Application of the random forest machine learning algorithm for arrhythmia alarm classification.
  • Investigation of various probability and class assignment techniques within ensemble models.
  • Development of a synthetic measure utilizing rank-based aggregation to guide decision paths for optimal classification.
  • Evaluation of performance using Area Under the Curve (AUC) and global accuracy metrics.

Main Results:

  • The proposed synthetic measure effectively leveraged classification results in the ensemble model.
  • The best performance achieved was 100% AUC accuracy for extreme tachycardia detection.
  • The poorest performance was observed for ventricular tachycardia, with 87% AUC and 51% accuracy.
  • Extreme tachycardia alarms were detected with 91% accuracy using the global score.

Conclusions:

  • The random forest method, enhanced by a novel synthetic measure, shows significant potential in reducing false arrhythmia alarms.
  • The approach offers a promising strategy for improving the reliability of clinical alarm systems in ICUs.
  • Further refinement is needed to improve detection rates for specific arrhythmias like ventricular tachycardia.