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Related Concept Videos

Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Utilizing Intensive Care Alarms for Machine Learning.

Anne Rike Flint1, Sophie A I Klopfenstein1,2, Patrick Heeren1

  • 1Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany.

Studies in Health Technology and Informatics
|May 25, 2022
PubMed
Summary
This summary is machine-generated.

Reducing non-actionable alarms in intensive care units (ICUs) is crucial for patient safety. This study details pre-processing steps for machine learning (ML) to classify alarms, aiming to improve patient monitoring and reduce alarm fatigue.

Keywords:
Alarm managementmachine learningpatient monitoring

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Clinical Monitoring

Background:

  • Alarms in intensive care units (ICUs) are vital for detecting critical medical conditions and enhancing patient safety.
  • A significant challenge is the high rate of non-actionable alarms (up to 99%), which do not necessitate immediate medical intervention.
  • This high false alarm rate contributes to alarm fatigue among healthcare professionals and can desensitize them to critical alerts.

Purpose of the Study:

  • To establish a foundation for machine learning (ML) applications aimed at reducing non-actionable alarms in ICUs.
  • To develop and present the technical and medical pre-processing methodologies required for alarm data annotation.
  • To differentiate between actionable and non-actionable alarms for improved patient monitoring and alarm management.

Main Methods:

  • Retrospective analysis of alarm data from intensive care units.
  • Development of technical pre-processing pipelines for raw alarm data.
  • Implementation of medical pre-processing steps for expert annotation of alarms as actionable or non-actionable.

Main Results:

  • Successfully outlined the pre-processing workflow for alarm data annotation.
  • Created a dataset annotated for actionability, suitable for training machine learning models.
  • Demonstrated the feasibility of preparing alarm data for ML-based reduction of non-actionable alerts.

Conclusions:

  • The pre-processing steps are essential for creating reliable datasets for machine learning in alarm management.
  • Accurate annotation of alarms is a critical prerequisite for developing effective ML solutions to reduce non-actionable alerts.
  • This work provides a basis for future research in ML-driven improvements to ICU alarm systems and patient safety.