Using Fuzzy Logic for Decision Support in Vital Signs Monitoring

Shohas Dutta; Anthony Maeder; Jim Basilakis

Using Fuzzy Logic for Decision Support in Vital Signs Monitoring

Shohas Dutta, Anthony Maeder, Jim Basilakis

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

This research investigated whether a fuzzy logic rule-based decision support system could be used to detect potentially abnormal health conditions, by processing physiological data collected from vital signs monitoring devices. An application of the system to predict postural status of a person was demonstrated using real data, to mimic the effects of body position changes while doing certain normal daily activities. The results gathered in this experiment achieved accuracies of >85%. Applying this type of fuzzy logic approach, a decision system could be constructed to inform necessary actions by caregivers or for a person themself to make simple care decisions to manage their health situation. .

Original language	English
Pages	29-33
Number of pages	5
Publication status	Published - 2013
Event	Australasian Workshop on Artificial Intelligence in Health (AIH 2013) and International Workshop on Collaborative Agents - Research and Development (CARE 2013) - Duration: 3 Dec 2013 → …

Conference

Conference	Australasian Workshop on Artificial Intelligence in Health (AIH 2013) and International Workshop on Collaborative Agents - Research and Development (CARE 2013)
Period	3/12/13 → …

Keywords

Assistive technologies
Care management
Decision support
Fuzzy logic
Patient monitoring

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title = "Using Fuzzy Logic for Decision Support in Vital Signs Monitoring",

abstract = "This research investigated whether a fuzzy logic rule-based decision support system could be used to detect potentially abnormal health conditions, by processing physiological data collected from vital signs monitoring devices. An application of the system to predict postural status of a person was demonstrated using real data, to mimic the effects of body position changes while doing certain normal daily activities. The results gathered in this experiment achieved accuracies of >85%. Applying this type of fuzzy logic approach, a decision system could be constructed to inform necessary actions by caregivers or for a person themself to make simple care decisions to manage their health situation. .",

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Using Fuzzy Logic for Decision Support in Vital Signs Monitoring. / Dutta, Shohas; Maeder, Anthony; Basilakis, Jim.

2013. 29-33 Paper presented at Australasian Workshop on Artificial Intelligence in Health (AIH 2013) and International Workshop on Collaborative Agents - Research and Development (CARE 2013), .

Research output: Contribution to conference › Paper › peer-review

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T1 - Using Fuzzy Logic for Decision Support in Vital Signs Monitoring

AU - Dutta, Shohas

AU - Maeder, Anthony

AU - Basilakis, Jim

PY - 2013

Y1 - 2013

N2 - This research investigated whether a fuzzy logic rule-based decision support system could be used to detect potentially abnormal health conditions, by processing physiological data collected from vital signs monitoring devices. An application of the system to predict postural status of a person was demonstrated using real data, to mimic the effects of body position changes while doing certain normal daily activities. The results gathered in this experiment achieved accuracies of >85%. Applying this type of fuzzy logic approach, a decision system could be constructed to inform necessary actions by caregivers or for a person themself to make simple care decisions to manage their health situation. .

AB - This research investigated whether a fuzzy logic rule-based decision support system could be used to detect potentially abnormal health conditions, by processing physiological data collected from vital signs monitoring devices. An application of the system to predict postural status of a person was demonstrated using real data, to mimic the effects of body position changes while doing certain normal daily activities. The results gathered in this experiment achieved accuracies of >85%. Applying this type of fuzzy logic approach, a decision system could be constructed to inform necessary actions by caregivers or for a person themself to make simple care decisions to manage their health situation. .

KW - Assistive technologies

KW - Care management

KW - Decision support

KW - Fuzzy logic

KW - Patient monitoring

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UR - http://ceur-ws.org/Vol-1098/aih2013_Dutta.pdf

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