| Literature DB >> 29058214 |
Jinwei Bai1, Li Shen2, Huimin Sun3, Bairong Shen4.
Abstract
Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.Keywords: Data mining for healthcare; Participatory medicine; Physiological informatics; Smartphone; Wearable sensors
Mesh:
Year: 2017 PMID: 29058214 DOI: 10.1007/978-981-10-6041-0_2
Source DB: PubMed Journal: Adv Exp Med Biol ISSN: 0065-2598 Impact factor: 2.622