| Literature DB >> 29431176 |
Abstract
According to a study done in 2014 by National Health Interview Survey around 6.3 million children in United States suffer from asthma [1]. Asthma remains one of the leading reasons for pediatric admissions to children's hospitals, and has a prevalence rate of approximately 10% in children and it leads to missed days from school and other societal costs. This occurs despite improved medications to control asthma symptoms. Asthma management is challenging as it involves understanding asthma causes and avoiding asthma triggers that are both multi-factorial and individualistic in nature. It is almost impossible for doctors to constantly monitor each patient's health and environmental triggers. According to a recent article, the IoT device market in health-care will increase to a worth of $117 billion by the year 2020 [2]. The monitoring segment of IoT devices have predicted to increase $15 billion in 2017 [5]. The sales of smart watches, fitness and health trackers, are expected to account for more than 70% of all wearables sale worldwide in 2016 [6]. According to IBM, the volume of health-care data has reached to 150 exabytes in 2017 [7]. The data generated from these consumer graded devices is increasing day by day. This data collection has exacerbated the problem of understanding the data and making sense of it.Entities:
Year: 2017 PMID: 29431176 PMCID: PMC5806526 DOI: 10.1109/SMARTCOMP.2017.7947025
Source DB: PubMed Journal: Proc Int Conf Smart Comput SMARTCOMP