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.
CITATION STYLE
Xiong, N., & Funk, P. (2016). Towards a probabilistic method for longitudinal monitoring in health care. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 187, pp. 30–35). Springer Verlag. https://doi.org/10.1007/978-3-319-51234-1_5
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