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kHealth: Semantic Multi-sensory Mobile Approach to Personalized Asthma Care

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P7: A New Paradigm for Health Care in the 21st Century
Scientific Session at AAAS2019 Annual Meeting
https://aaas.confex.com/aaas/2019/meetingapp.cgi/Session/21133

Asthma is a chronic multifactorial disease and traditional clinical practice requires patients to meet their clinician in a timely yet infrequently meetings scheduled once in 3-6 months depending on the patient’s condition. The clinical diagnosis relies on the patient’s description of their current health condition. The patient’s description need not be accurate at times and may lack some important aspects needed for accurate diagnosis. We at Kno.e.sis work with clinicians and their pediatric asthma patients at the Dayton Children's Hospital to evaluate an IoT/mobileApp enabled personalized digital health management. We built a kHealth system for continuous monitoring and improved tracking of 30 parameters including the child’s symptoms, activities, sleep, and treatment adherence. It can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness.
More at: https://aaas.confex.com/aaas/2019/meetingapp.cgi/Paper/23000

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kHealth: Semantic Multi-sensory Mobile Approach to Personalized Asthma Care

  1. 1. Prof. Amit. P. Sheth LexisNexisOhioEminentScholar, ExecutiveDirector, Kno.e.sis WrightStateUniversity Background: http://bit.ly/k-APH,http://bit.ly/kAsthma,http://j.mp/PARCtalk Icon source used in the entire presentation - https://thenounproject.com kHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care P7: A New Paradigm for Health Care in the 21st Century AAAS 2019 Annual Meeting: Washington DC February 15, 2019
  2. 2. Internet of Medical Things ● Episodic to Continuous Monitoring ● Clinician-centric to Patient-centric ● Clinician controlled to Patient-empowered ● Disease Focused to Wellness-focused ● Sparse data to Multimodal Big Data Medical Internet of Things
  3. 3. Augmented Personalised Health Big Data to Smart Data Augmented Personalised Health(APH) is a vision to enhance the healthcare by using AI techniques on semantically integrated PGHD, environmental data, clinical data, public health data & social data. Data Components PGHD, Clinical data, environmental data and Social Data Smart Data meaningful data obtained after contextualised processing Image Source - http://www.dqchannels.com/big-data-and-smart-data-big-drivers-for-smart-decision-making/
  4. 4. 1. Self Monitoring Constant and remote monitoring of disease specific health indicators for any given patient 2. Self Appraisal Interpretation of the data collected with respect to disease context for the patient to evaluate themselves 3. Self Management Identify the deviation from normal and assist patients to get back to prescribed care plan 4. Intervention Change in the care plan - with the converted smart data by APH, provide decision support for treatment adjustments 5. Disease Progression and Tracking Longitudinal data collection and analysis to enhance patients health over the time Sheth, et.al. How will the Internet of Things enable Augmented Personalized Health? Health Management Strategies of APH
  5. 5. kHealth Asthma: A multisensory approach for personalised asthma care in children
  6. 6. Data Collection so far 110 patients 30 parameters 1852 data points per patient per day 66% Kit compliance ● Data Collection: Since Dec 2016 ● Active sensing: 18 data points/day (Peak flow meter and Tablet) ● Passive sensing: 1834 data points/ day (Foobot, Fitbit, Outdoor environmental data) 5-17 years of age 1 or 3 months of monitoring
  7. 7. Utkarshani Jaimini, Krishnaprasad Thirunarayan, Maninder Kalra, Revathy Venkataramanan, Dipesh Kadariya, Amit Sheth, “How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma”, JMIR Pediatr Parent 2018;1(2):e11988, DOI: 10.2196/11988.
  8. 8. Asthma Digital Phenotypes Digital Phenotype Score = Symptom Score + Rescue Score + Activity Score + Awakening Score
  9. 9. Under review at JAMIA
  10. 10. Determination of Personalized triggers
  11. 11. Evidence based Path to Personalization Absenceof Pollen First 6 weeks Presenceof Pollen Rest of the 7 weeks LearningPeriod 4 weeks PredictionPeriod 2 weeks LearningPeriod 4 weeks PredictionPeriod 3 weeks Pollen 0 Pollen 0 days Pollen 17 days Pollen 3 days PM2.5 20 days PM2.5 5 days PM2.5 14 days PM2.5 2 days Ozone 1 day Ozone 0 Ozone 0 Ozone 1 day Asthma Episodes 21 days Asthma Episodes 5 days Asthma Episodes 17 days Asthma Episodes 3 days Patient-A was monitored for 13 weeks encompassing winter to spring 2018 ● Absence of Pollen - PM2.5 is the trigger ● Presence of Pollen - Pollen and PM2.5. Severe symptoms occurred in this period. Presence of both PM2.5 and Pollen increased the intensity of asthma episodes
  12. 12. APH for kHealth Asthma 1. Self Monitoring Show me the data that is relevant to asthma (Eg:AQI, Pollen) 2. Self Appraisal What is my asthma control level? 3. Self Management Given all my asthma related data, do I need to take SABA(Short Acting Beta Agonists)? 4. Intervention Based on poor asthma control, the system encourages clinical consultation(before routine examination). 5. Disease Progression and Tracking How was my asthma control since last year?
  13. 13. kBOT for kHealth Asthma Many Sources of Highly Diverse Data (& collection methods: Active + Passive): Up to 1852 data points/ patient /day kBot with screen interface for conversation Images Text Speech ★ Episodic to Continuous Monitoring ★ Clinician-centric to Patient-centric ★ Clinician controlled to Patient-empowered ★ Disease Focused to Wellness-focused ★ Sparse data to Multimodal Big Data *(Asthma-Obesity)
  14. 14. Contextualization and Personalization kBOT initiates greeting conversation. Understands the patient’s health condition (allergic reaction to high ragweed pollen level) via the personalized patient’s knowledge graph generated from EMR, PGHD, and prior interactions with the kBOT. Generates predictions or recommended course of actions. Inference based on patient’s historical records and background health knowledge graph containing contextualized (domain-specific) knowledge. Figure: Example kBOT conversation which utilizes background health knowledge graph and patient’s knowledge graph to infer and generate recommendation to patients. ★ Conversing only information relevant to the patient Context enabled by relevant healthcare knowledge including clinical protocols.
  15. 15. kHealth TeamMembers Revathy Venkataramanan (Graduate Student) Utkarshani Jaimini (Graduate Student) Hong Yung Yip (Graduate Student) Dipesh Kadaria (Graduate Student) Tanvi Banerjee (Faculty) Dr. KrishnaPrasad Thirunarayan (Faculty) Dr. Maninder Kalra (Pulmonologist at Dayton Children’s Hospital) Clinical collaborator Acknowledgement: This research is supported by NICHD/NIH under the Grant Number: 1R01HD087132. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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