False alarm reduction in critical care
Name
nihms-807599.pdf
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511.86 KB
Format
Adobe PDF
Checksum (MD5)
67f44331b1cba1ccf09a2288d9d4ff9d
Author(s)
Clifford, Gari D
Kella, Danesh
Chahin, Abdullah
Kooistra, Tristan
Perry, Diane
Silva, Ikaro
Moody, Benjamin Edward
Mark, Roger G
Li, Qiao,S. M.Massachusetts Institute of Technology.
Date Issued
July 2016
Journal
Physiological Measurement
Publisher
IOP Publishing
Citation
Clifford, Gari D et al. “False Alarm Reduction in Critical Care.” Physiological Measurement 37, 8 (July 2016): E5–E23 © 2016 Institute of Physics and Engineering in Medicine
Version
Author's final manuscript
Abstract
High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1088/0967-3334/37/8/E5
