- Title
- An ensemble of machine learning and clinician set thresholds for vital signs alarms
- Creator
- Mai, Shenhan; Balasubramanian, Venki; Arora, Teena
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187843
- Identifier
- vital:17152
- Identifier
-
https://doi.org/10.1145/3511616.3513121
- Identifier
- ISBN:9781450396066 (ISBN)
- Abstract
- High false alarm rates is a common issue in patient vital sign monitoring systems and may result in alarm fatigue for medical workers and even cause alarm-related patient deaths. In this study, the research toward the use of ensemble learning that combines a feed forward back propagation neural network, a random forest and manually set threshold based alarms is used. A method for estimating the false alarm rate using the machine learning, to help clinicians set thresholds is also proposed. Experimental results to date on a small dataset are promising. © 2022 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 232-234
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 ACM
- Rights
- Open Access
- Subject
- Alarm fatigue; Ensemble learning; False alarm; Machine learning; Manual threshold; Neural network; Random forest
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE2 | Accepted version | 329 KB | Adobe Acrobat PDF | View Details Download |