Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs

Computing and Communications

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs. / Woo, Wai Lok; Koh, BHD; Gao, Bin et al.
Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things. 2020. p. 206-211 (ACM International Conference Proceeding Series).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Woo, WL, Koh, BHD, Gao, B, Nwoye, EO, Wei, B & Dlay, SS 2020, Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs. in Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things. ACM International Conference Proceeding Series, pp. 206-211.

APA

Woo, W. L., Koh, BHD., Gao, B., Nwoye, EO., Wei, B., & Dlay, SS. (2020). Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs. In Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things (pp. 206-211). (ACM International Conference Proceeding Series).

Vancouver

Woo WL, Koh BHD, Gao B, Nwoye EO, Wei B, Dlay SS. Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs. In Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things. 2020. p. 206-211. (ACM International Conference Proceeding Series).

Author

Woo, Wai Lok ; Koh, BHD ; Gao, Bin et al. / Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs. Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things. 2020. pp. 206-211 (ACM International Conference Proceeding Series).

Bibtex

@inproceedings{ed2e95bed61e48f8a296616e4db42e9c,

title = "Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs",

abstract = "This study develops a multivariable data analysis technique for subjects who are monitored for four vital signs, namely heart rate, respiration rate, blood pressure and body temperature, and proposes a method to provide early warning of abnormal health condition and visual analytics that identifies the contributing factor that causes the early warning. It proposes the use of the deranged values of the vital signs of a collection of subjects to fix a multivariable PCA model and set the control level based on the Hotelling T2 statistics. The test subject is monitored using the model and the control level. The values are deemed abnormal when the multivariable observation exceeds the control limit by two consecutive observations, and an early warning is issued when there are multiple of such detections. The study shows that there is statistical significance between the distribution of the number of detections in subjects who are well enough to be discharged from ICU and those whose conditions are so bad that they did not survive the stay in the ICU.",

author = "Woo, {Wai Lok} and BHD Koh and Bin Gao and EO Nwoye and Bo Wei and SS Dlay",

year = "2020",

month = apr,

day = "24",

language = "English",

series = "ACM International Conference Proceeding Series",

pages = "206--211",

booktitle = "Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things",

}

RIS

TY - GEN

T1 - Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs

AU - Woo, Wai Lok

AU - Koh, BHD

AU - Gao, Bin

AU - Nwoye, EO

AU - Wei, Bo

AU - Dlay, SS

PY - 2020/4/24

Y1 - 2020/4/24

N2 - This study develops a multivariable data analysis technique for subjects who are monitored for four vital signs, namely heart rate, respiration rate, blood pressure and body temperature, and proposes a method to provide early warning of abnormal health condition and visual analytics that identifies the contributing factor that causes the early warning. It proposes the use of the deranged values of the vital signs of a collection of subjects to fix a multivariable PCA model and set the control level based on the Hotelling T2 statistics. The test subject is monitored using the model and the control level. The values are deemed abnormal when the multivariable observation exceeds the control limit by two consecutive observations, and an early warning is issued when there are multiple of such detections. The study shows that there is statistical significance between the distribution of the number of detections in subjects who are well enough to be discharged from ICU and those whose conditions are so bad that they did not survive the stay in the ICU.

AB - This study develops a multivariable data analysis technique for subjects who are monitored for four vital signs, namely heart rate, respiration rate, blood pressure and body temperature, and proposes a method to provide early warning of abnormal health condition and visual analytics that identifies the contributing factor that causes the early warning. It proposes the use of the deranged values of the vital signs of a collection of subjects to fix a multivariable PCA model and set the control level based on the Hotelling T2 statistics. The test subject is monitored using the model and the control level. The values are deemed abnormal when the multivariable observation exceeds the control limit by two consecutive observations, and an early warning is issued when there are multiple of such detections. The study shows that there is statistical significance between the distribution of the number of detections in subjects who are well enough to be discharged from ICU and those whose conditions are so bad that they did not survive the stay in the ICU.

M3 - Conference contribution/Paper

T3 - ACM International Conference Proceeding Series

SP - 206

EP - 211

BT - Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things

ER -

Research