A decision support system for reducing false alarms in ICU

From ISLAB/CAISR
Title A decision support system for reducing false alarms in ICU
Summary Developing a clinical decision support system using machine learning and biomedical signal analysis techniques for an ICU setting.
Keywords Machine learning, Biomedical signal processing, Clinical decision support, Temporal data analysis
TimeFrame Winter 2016 / Spring 2017
References 1. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiological Measurement. 2016;37(8):1298.

2. Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: a review of medical device alarm management. Biomedical Instrumentation & Technology. 2012;46(6):478-87.

3. Cvach M. Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology. 2012;46(4):268-77.

4. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000;101(23):e215.

Prerequisites Good knowledge of applied mathematics and signal processing. An ability to implement state-of-the-art algorithms in a suitable programming environment. An interest in machine learning algorithms.
Author
Supervisor Sławomir Nowaczyk, Awais Ashfaq
Level Master
Status Open

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Background: With the advent of advanced patient monitoring systems in healthcare, alarms are ubiquitous and have been a subject of technical and psychological research for decades. False alarms in intensive care monitors have been reported to approach up to 86% (1). These ceaseless noises supplement the already stressful ICU environment causing sleep deprivation, sensory overload, desensitization to alarms, reduced quality of care and a depressed immune system. More about ICU alarm fatigue can be found in (1-3)

Objective: The desired objective of the project is to develop an intelligent decision support system that reduces the number of false alarms using ICU patient data. The datasets include the following:

  • Waveforms: such as multi-channel ECG, multiple blood pressure recordings (like ICP, LAP, CVP, LAP etc.), Plethysmography and Respiratory waveforms.
  • Numeric data: Body temperature, Cardiac Output, Heart Rate, Respiration Rate measured at equal or random intervals.

The student shall explore a wide range of exciting new ideas in the field of multi-channel data fusion and clinical decision support systems such as evidence-based learning, random forests, deep learning etc.

Research Questions:

  • How to combine different heterogeneous sources of temporal information?
  • How to interpret such models to extract the underlying understanding of the classifier output?

Project Plan: The student will have a lot of freedom in choosing the focus of the project, however, the suggested plan is as follows:

  • Investigate state-of-the-art Clinical Decision Support Systems (CDSS) and evaluate how they can be adapted to an ICU setting. These include knowledge based and non-knowledge based CDSS.
  • Explore a wide range of machine learning algorithms that have been studied in the context of non-knowledge based CDSS and look for ways how they may be integrated with the domain knowledge.
  • Develop a working prototype that can classify false alarms among all vital alarms.
  • Test the developed prototype on pre-recorded ICU data, or if possible, in real ICU setting and report suggestions for improvements.

Data needed for the project:

  • Labelled and unlabeled data for semi-supervised learning – available at (4)
  • ICU patient data for testing – available at (4). The developed prototype might also be put to test in real ICU setting in Halland Hospital for quality analysis.

Contact

  • Slawomir Nowaczyk (slawomir.nowaczyk@hh.se)
  • Awais Ashfaq (awais.ashfaq@hh.se)