The proliferation of smartphones and sensors has made continuous monitoring of physiology, environment, and public health notifications possible. However, personalized digital health and patient empowerment will become reality only if massive multisensory and multimodal observational data is processed within patient context and made actionable to enable early detection; better diagnosis, effective prevention and treatment of the disease; and improve the quality of life. This research will be performed in the context of asthma, which affects 300 million people and claims over 250,000 lives worldwide annually. According to CDC, 9.3% of US children have asthma. Understanding asthma and its management is challenging; it is a multifactorial disease with subjective causes and symptoms, and extant diagnosis and treatment guidelines can be improved using evidence-based action recommendation. With the advent of mobile and connected sensors, timely information about asthma triggers has become available, and its exploitation requires precisely understanding and quantifying the sensed data's role for preventive and proactive assessment of asthma. In this application led by computer scientists and a practicing clinician, we address the central problem of deriving actionable information from data monitored using kHealth technology. Through a pilot study, we have tested our kHealth kit equipped on several pediatric patients under the guidance of a clinical doctor for reliable data collection and patient operation, with promising initial results. We build on this foundation by addressing the problem of semantic representation, integration, abstraction, and personalized interpretation of heterogeneous observations spanning physiological, environmental data, and online data in the context of medical knowledge. Our primary hypothesis is that an evidence-based approach to asthma can help doctors determine more precisely the cause, severity and control of asthma, and can alert patients to seek timely clinical assistance to better manage asthma.

Public Health Relevance

This application direclty relates to NICHD mission of improving health of children, with focus on asthma that affects a significant population. The kHealth system uses multiple sensors, mobile application and intelligent, personalized processing to predict health risks associated with asthma in children to develop a preventative and proactive approach to asthma management.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Tamburro, Robert F
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Wright State University
Biostatistics & Other Math Sci
Biomed Engr/Col Engr/Engr Sta
United States
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Sheth, Amit; Jaimini, Utkarshani; Yip, Hong Yung (2018) How Will the Internet of Things Enable Augmented Personalized Health? IEEE Intell Syst 33:89-97
Sheth, Amit; Perera, Sujan; Wijeratne, Sanjaya et al. (2017) Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. Proc IEEE WIC ACM Int Conf Web Intell Intell Agent Technol 2017:1-9
Miller, Michele; Banerjee, Tanvi; Muppalla, Roopteja et al. (2017) What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention. JMIR Public Health Surveill 3:e38
Jaimini, Utkarshani; Banerjee, Tanvi; Romine, William et al. (2017) Investigation of an Indoor Air Quality Sensor for Asthma Management in Children. IEEE Sens Lett 1:
Jaimini, Utkarshani (2017) PhD Forum: Multimodal IoT and EMR based Smart Health Application for Asthma Management in Children. Proc Int Conf Smart Comput SMARTCOMP 2017:
Sheth, Amit; Jaimini, Utkarshani; Thirunarayan, Krishnaprasad et al. (2017) Augmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare. RTSI 2017: