KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care
- 1 Motivation and Background
- 2 kHealth Vision
- 3 Asthma: Challenges and Opportunities
- 4 kHealth for Asthma
- 5 Funding
- 6 People
- 7 Related kHealth Projects
- 8 kHealth Observations
- 9 Preliminary Data Analysis
- 10 IRB
- 11 kHealth User Manual
- 12 kHealth App Introduction
- 13 Related Talks and Presentations
- 14 Publications
- 15 References
Motivation and Background
More than 25 million people in the U.S. are diagnosed with asthma, out of which 7 million are children . Asthma related healthcare costs alone are around $50 billion a year . Current reactive healthcare costs more than 17% of GDP in the US [3, 4]. Specifically, with the current reactive care for asthma, there were 155,000 hospital admissions and 593,000 ER visits in 2006 . It is estimated that, by 2025, over 400 million people will be affected by asthma worldwide. With increasing adoption of mobile devices and low-cost sensors, an unprecedented amount of data is being collected by people . This data collection has exacerbated the problem of understanding the data and making sense of it. In this project, we explore the role of knowledge empowered algorithms in making sense of this data deluge in the context of asthma assessment and management.
Digital health and mobile health applications are benefitting from semantic web research from Wright State's Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis). Director of Kno.e.sis and Professor of Computer Science and Engineering Dr. Amit Sheth describes development of mobile health applications with sensor technology to monitor patient health, mobile computational support, and clear feedback to the patient and physician.
Amit Sheth, Pramod Anantharam, Krishnaprasad Thirunarayan, "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Workshop on Personal Data Analytics in the Internet of Things at VLDB2014, Hangzhou, China, September 5, 2014.
Asthma: Challenges and Opportunities
Asthma is a great example of a problem that spans Physical-Cyber-Social (PCS) systems. The health signals related to asthma spans Physical (environmental), Cyber (CDC reports), and Social (asthma/symptom reports on social media) modalities. Specifically, for asthma, we group health signals as personal (wheezing level, exhaled Nitric Oxide), population (asthma reports on social media), and public health signals (CDC asthma reports).
kHealth for Asthma
We tackle this important problem by a combination of active and passive sensing. Active sensing involves the patient in the loop (obtrusive) while the passive sensing does not involve patient involvement (unobtrusive). Using a novel approach of utilizes low-cost sensors for continuous monitoring (active and passive sensing), we propose to develop algorithms that can take this multi-modal data and translate them to practical and actionable information for asthma patients and their healthcare provider. Specifically, provide information on asthma control level based on symptoms and their severity, asthma triggers and early alerts for increasing asthma symptoms.
- Principal Investigators:
- Amit P. Sheth (Kno.e.sis, Wright State University)
- Graduate Students from Kno.e.sis, Wright State University:
Contact: Utkarshani Jaimini
Related kHealth Projects
- kHealth overview with example of ADHF
- kHealth for dementia
- kHealth for reducing liver cirrhosis readmission (to come)
Asthma is a multi-faceted problem and we propose a holistic solution for
Physiological: Wheezometer , Nitric Oxide , Accelerometer, Microphone, Contextual Questions
Environmental: Sensordrone , Dust Sensor , Location
CDC , DCHC’s EMR Records (periodic manual review)
Everyaware , AirQuality Egg , Allergy Alerts [13,14], Social Observations (e.g., tweets), Air Quality Index
Preliminary Data Analysis
kHealth kit could be used to collect observations (both sensor and patients questionnaire response) in the patient home environment (which was never accessible in a quantitative form to doctors). These observations when collected based on expert guidance, prove valuable for clinical decision support. These observations when interpreted by a doctor, lead to some interesting insights:
*Activity limitation is likely related to high exhaled Nitric Oxide
*Low exhaled Nitric Oxide observed with absence of coughing
*Activity limitation observed with high pollen activity
THESE PRELIMINARY FINDINGS NEED TO BE VALIDATED IN LARGER STUDIES
We collect observations from three sensors (temperature, humidity, Carbon monoxide) on Sensordrone at the rate of 1 Hz (1 observation / second). Nitric Oxide observations from the NODE sensor are collected at the rate of 2 observations / day. Patients answer a questionnaire which has 5 questions resulting in 5 observations / day. For a single patient, we collect over 250,000 observations / day. In our study of three patients, we have collected over 9 million data points.
Dayton Children's Hospital Institutional Review Board (IRB) approved the pilot study in October 2013 which began enrolling pediatric patients and their parents to use the kHealth kit for Asthma. IRB continuation was approved in October 2014. Please contact Prof. Amit Sheth [amit at knoesis.org] or Dr. Shalini Forbis [ForbisS at childrensdayton.org] to obtain the exact copy of IRB.
kHealth User Manual
kHealth App Introduction
Related Talks and Presentations
 Amit Sheth. Ontology-enabled Healthcare Applications Exploiting Physical-Cyber-Social Big Data. In Ontology Summit 2016. Virtual; 2016.
Pramod Anantharam, Tanvi Banerjee, Amit Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan, Shalini Forbis. Knowledge-driven Personalized Contextual mHealth Service for Asthma Management in Children. 2015 IEEE International Conference on Mobile Services. New York, NY: IEEE; 2015. p. 284 - 291.
 Amit Sheth. Smart Data: How You and I Will Exploit Big Data for Personalized Digital Health and Many Other Activities. 2015.
 Amit Sheth, Pramod Anantharam, Krishnaprasad Thirunarayan, "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Workshop on Personal Data Analytics in the Internet of Things at VLDB2014, Hangzhou, China, September 5, 2014.
 NHLBI description of Asthma, Available online at: http://www.nhlbi.nih.gov/health/health-topics/topics/asthma/
 Asthma related facts, Available online at: http://www.lung.org/lung-disease/asthma/resources/facts-and-figures/asthma-in-adults.html
 Squires, D. A., The U.S. Health System in Perspective: A Comparison of Twelve Industrialized Nations, June 2011, Available online at: http://bit.ly/oZwhFZ
 Health Costs: How the U.S. Compares With Other Countries, Available online at: http://www.pbs.org/newshour/rundown/2012/10/health-costs-how-the-us-compares-with-other-countries.html
 Quantified Self http://quantifiedself.com/
 Wheezometer by iSonea, Available online at: http://www.isoneamed.com/wheezometer.html
 Nitric Oxide Sensor, Available online at: http://nodesensors.com/product/oxa-gas-sensor-nitric-oxide-no/
 Sensordrone, a bluetooth enabled low-cost sensor for monitoring the environment, Available online at: http://www.kickstarter.com/projects/453951341/sensordrone-the-6th-sense-of-your-smartphoneand-be/
 Optical Dust Sensor, Available online at: https://www.sparkfun.com/products/9689
 Centers for Disease Control and Prevention, Available online at: http://www.cdc.gov/
 Everyaware, Sensing Air Pollution, Available online at: http://www.everyaware.eu/activities/case-studies/air-quality/
 Community-led sensing of AirQuality, Available online at: http://airqualityegg.com/
 National and Local Allergy Forecast, Available online at: http://www.pollen.com/allergy-weather-forecast.asp
 National Allergy Bureau Alerts, Available online at: http://www.aaaai.org/global/nab-pollen-counts.aspx
 Air Quality Index from United States Environmental Protection Agency, Available online at : http://www.epa.gov/
 Akinbami et al. (2009). Status of childhood asthma in the United States, 1980–2007. Pediatrics,123(Supplement 3), S131-S145.