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Decision Tree Predictive Learner-Based Approach for False Alarm Detection in ICU.

Tishya Manna1, Aleena Swetapadma2, Moloud Abdar3

  • 1School of Computer Engineering, KIIT University, Bhubaneswar, India.

Journal of Medical Systems
|May 23, 2019
PubMed
Summary
This summary is machine-generated.

A new method accurately detects false alarms in Intensive Care Units (ICUs) using electrocardiogram (ECG) and other signals. This approach achieves 97% accuracy, promising improved patient monitoring and reduced alarm fatigue.

Keywords:
ArrhythmiaDecision tree predictive learnerECGFalse alarmICUMachine learning

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • False alarms in Intensive Care Units (ICUs) during arrhythmia monitoring can lead to alarm fatigue and desensitize clinicians.
  • Existing methods for arrhythmia detection may not adequately address the issue of false alarms, impacting clinical workflow efficiency.

Purpose of the Study:

  • To propose and evaluate a novel method for accurate false alarm detection in ICU arrhythmia monitoring.
  • To develop an assumption-based classification approach for enhancing the reliability of critical care monitoring systems.

Main Methods:

  • Utilized multiple physiological signals including electrocardiogram (ECG), atrial blood pressure (ABP), photoplethysmogram (PLETH), and respiration (RESP).
  • Employed a decision tree predictive learner (DTPL) based classifier for analyzing input signals and detecting false alarms.
  • Developed a novel assumption-based classification strategy for improved detection accuracy.

Main Results:

  • The proposed method achieved a high accuracy of 97% in predicting false alarms in the ICU setting.
  • The DTPL classifier effectively integrated diverse physiological inputs for robust false alarm identification.

Conclusions:

  • The developed method demonstrates significant promise for effective false alarm detection in ICUs.
  • This novel assumption-based classification approach offers a valuable tool for reducing unnecessary alarms and improving patient care.
  • The findings suggest potential for widespread adoption in critical care monitoring systems.