Research Article
Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
250 downloads
@INPROCEEDINGS{10.1007/978-3-319-51234-1_5, author={Ning Xiong and Peter Funk}, title={Towards a Probabilistic Method for Longitudinal Monitoring in Health Care}, proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers}, proceedings_a={HEALTHYIOT}, year={2017}, month={1}, keywords={Health monitoring Longitudinal signal Symbolic time series Markov model Case-based reasoning}, doi={10.1007/978-3-319-51234-1_5} }- Ning Xiong
Peter Funk
Year: 2017
Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-51234-1_5
Abstract
The advances in IoT and wearable sensors enable long term monitoring, which promotes earlier and more reliable diagnosis in health care. This position paper proposes a probabilistic method to address the challenges in handling longitudinal sensor signals that are subject to stochastic uncertainty in health monitoring. We first explain how a longitudinal signal can be transformed into a Markov model represented as a matrix of conditional probabilities. Further, discussions are made on how the derived models of signals can be utilized for anomaly detection and classification for medical diagnosis.
Copyright © 2016–2024 EAI
