- Title
- Prioritization of clinical alarms using semantic features of vital signs in remote patient monitoring
- Creator
- Arora, Teena; Balasubramanian, Venki; Mai, Shenhan
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187861
- Identifier
- vital:17134
- Identifier
-
https://doi.org/10.1145/3511616.3513124
- Identifier
- ISBN:9781450396066 (ISBN)
- Abstract
- In recent years remote patient monitoring applications have emerged that can monitor the patient continuously and remotely with the help of wearable sensors that collect physiological data and send it to a telemedicine platform. Sensitivity of the sensor, patient's movement, electromagnetic interference, and data processing algorithms are a few factors that affect the collected data, leading to false alarms, and consequent false alarm leads to alarm fatigue. This study presents novel factors such as trust, frequency, slope, and trend that transform the vital signs raw data from the sensors into semantic data in a remote monitoring application. Experimental results have shown that data transformations lead to a reduction in clinically non-significant alarms and the prioritization of clinically significant alarms. © 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. 242-245
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 ACM
- Subject
- False Alarms; Modified early warning score (MEWS); Patterned modified early warning score (PMEWS); Remote patient monitoring (RPM)
- Reviewed
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