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Augmented Personalized Health

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Paper: http://bit.ly/k-APH Video: https://youtu.be/wDi1mLLyxuc
Invited talk: @ Big Data Integration and IoT for Smart Health Care, 3rd Intl Forum on Research and Technologies for Society and Industry Modena Italy, 13 September 2017

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Augmented Personalized Health

  1. 1. Augmented Personalized Healthcare How Smart Data with IoTs and AI is about to change Healthcare Invited Talk @ Big Data Integration and IoT for Smart Health Care, 3rd Intl Forum on Research and Technologies for Society and Industry Modena Italy, 13 September 2017 Prof. Amit Sheth LexisNexis Ohio Eminent Scholar; Executive Director, Kno.e.sis Wright State University Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk,
  2. 2. 2 • Traditional Healthcare • Healthcare: Then and Now • Augmented Personalized Health for health care of the future, and associated technical challenges • kHealth: Three ongoing applications Outline
  3. 3. 3 Traditional Healthcare How are you feeling today?I am not feeling well since yesterday afternoon.
  4. 4. 4 Healthcare: Then and Now Episodic Continuous
  5. 5. 5 Healthcare: Then and Now Disease focussed Beyond medical intervention: Lifestyle change/holistic, Wellness, Quality of Life
  6. 6. 6 Healthcare: Then and Now Clinic centric Patient centric (Anywhere the patient is)
  7. 7. 7 Healthcare: Then and Now Clinician control Patient empowered
  8. 8. 8 Healthcare: Then and Now Limited data 360 degree multimodal ● Personal-Public-Population ● Physical-Cyber-Social big data driven
  9. 9. The Patient of the Future MIT Technology Review, 2012 http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/9 Patients are increasingly taking control of own health Patient Generated Health Data (PGHD) are “health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern. PGHD include, but are not limited to health history, treatment history, biometric data, symptoms, and lifestyle choices.” Office of the National Coordinator for Health Information Technology (ONC).
  10. 10. Google:Verily Life Sciences https://blog.verily.com/2017/04/introducing-verily-study-watch.html
  11. 11. AIM: Doctor in a self-driving Car [Image: courtesy Artefac, https://flipboard.com/@flipboard/-who-needs-a-hospital-when-this-self-dri/f-4204314e11%2Ffastcodesign.com t]
  12. 12. 12 Converting big data into smart data through contextual and personalized processing such that patient and clinician can make better decisions and take timely actions for Augmented Personalized Health Future Health Care
  13. 13. 13 Augmented Personalized Healthcare (APH) is expected to enhance healthcare by personalizing the use of all relevant Physical, Cyber, and Social data obtained from wearables, sensors and Internet of Things, mobile applications, electronic medical records, web-based information, and social media for better health for an individual. Data include traditional clinical data, PGHD and public health data, as well as environmental and social data that could impact an individual’s health. Augmented Personalized Healthcare
  14. 14. 14https://cdn1.tnwcdn.com/wp-content/blogs.dir/1/files/2013/12/augmented-reality-doctors-lab.jpg Augmented Personalized Healthcare
  15. 15. 15 Providing actionable information in a timely manner is crucial to avoid information overload or fatigue Sleep data Community dataPersonal Schedule Activity data Personal health records Data Overload for Patients/health aficionados
  16. 16. 16 Challenges in deriving actionable insights from Sensor Data According to Forbes, the wearables market exceeded $2 billion in 2015, 3 billion in 2016 and will be over 4 billion in 2017. Leading to vast volume of healthcare data: some key issues ● Sensor reliability and Quality ● Sensor data Heterogeneity ● Contextual Interpretation and Abstraction ● Personalized Health and Health Objective https://www.linkedin.com/pulse/digital-health-why-doctors-should-care-doug-hart
  17. 17. 17 Sensor Data Reliability and Quality 1.https://wq.io/media/images/quality-large.png, 2https://readwrite.com/2017/05/26/study-wearables-counting-calories-dl4/, 3.https://www.wsj.com/articles/smartphones-open-a-new-world-for-medical-researchers-1498442821 ● Consumer graded devices can be terrible and inaccurate [2] ● Is data generated from them useful for health application? ● Research shows they can be effective for health applications [3]
  18. 18. 18 Is sensor data useful for Health Decision making?
  19. 19. 19 Sensor Data Heterogeneity Questionnaire Data Electronic Medical Records Gene Lab Test Wearables and Sensors To enable the proper interpretation of data and for determining remediation measures, it is essential to convert the data into abstractions ignoring inessential differences and providing an integrated view for the clinician to take action
  20. 20. Hyperthyroidism Elevated Blood Pressure Systolic blood pressure of 150 mmHg “150” .. . .. . Too much data does not help make timely decisions => Contextual Interpretation and Sensor Data Abstraction
  21. 21. Personalized Health and Objectives: one size does not fit all Millions of people - > one treatment Wearable and Sensor data
  22. 22. 2222 ACTIONS situation awareness useful for decision making Converting data to actions Hyperthyroidism Elevated Blood Pressure Systolic blood pressure of 150 mmHg “15 0” .. . .. . ABSTRACTIONS make sense to humans KNOWLEDGE for interpretation of observations Contextualization Personalization DATA Observations from machine and social sensors Kno.e.sis’ kHealth initiative follows this approach -- currently for asthma in children, bariatric surgery, ...
  23. 23. 23 • Asthma Management in Children • Bariatrics Surgery for Obese Adults • Pain Management kHealth Personalized Digital Health Initiative
  24. 24. kHealth Asthma Management
  25. 25. 25 kHealth: Health Signal Processing Architecture Personal level Signals Public level Signals Population level Signals Domain Knowledge Risk Model Events from Social streams Take Medication before going to work Avoid going out in the evening due to high pollen levels Contact doctor Analysis Personalized Actionable Information Data Acquisition & aggregation
  26. 26. 26 26Asthma Domain Knowledge Domain Knowledge ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2- agonist ; *consider referral to specialist Asthma Control and Actionable Information Asthma Control Daily Medication choices for stating therapy Not Well Controlled Poor Controlled Severity Level of Asthma Intermittent Asthma Mild Persistent Asthma Moderate Persistent Asthma Severe Persistent Asthma Recommended Action Recommended Action Recommended Action SABA prn Low dose ICS Medium ICS alone or with LABA/Montelukast High dose LABA/Montelukast Medium ICS Medium ICS + LABA/ Montelukast or High Dose ICS Need specialist care Medium ICS Medium ICS + LABA/ Montelukast or High Dose ICS Need specialist care
  27. 27. 27 Sensordrone – for monitoring environmental air quality Wheezometer – for monitoring wheezing sounds Can I reduce my asthma attacks at night? What are the triggers? What is the wheezing level? What is the propensity toward asthma? What is the exposure level over a day? Commute to work Decision Support for Doctors and Patients: A Scenario Luminosity CO level CO in gush during day time Actionable Information Personal level Signals Public level Signals Population level Signals What is the air quality indoors? Close the window at home during day to avoid CO2 inflow, to avoid asthma attacks at night
  28. 28. 28 k-Health Dashboard: A Platform to Visually Analyse to find Correlations (e.g., Patient Symptoms and Personalized Data) Multimodal Data Streams & Anonymised Patient Data Visualized for Correlation Analysis interpreted in with the help of knowledge graph (relevant medical knowledge)
  29. 29. 29 Activity limitation observed with high pollen activity
  30. 30. 30 Low exhaled nitric oxide observed with absence of coughing
  31. 31. 31 Medication use possibly leading to decreasing exhaled nitric oxide
  32. 32. 32 Activity Limitation is likely related to high exhaled nitric oxide
  33. 33. 33 Computing Predictors Medications Activity Temperature Humidity Pollen Air Quality Spirometry Outdoor, Indoor & Medical (Predictors) Logistic Regression Model [Ax1+Bx2+Cx3…..] Weights Computed Cough Cough Symptoms Outcome Prediction
  34. 34. 34 34 Risk assessment model Semantic Perception Personal level Signals Public level Signals Domain Knowledge Population level Signals GREEN -- Well Controlled YELLOW – Not well controlled Red -- poor controlled How controlled is my asthma? Patient Health Score (Diagnostic)
  35. 35. 35 35 Risk assessment model Semantic Perception Personal level Signals Public level Signals Domain Knowledge Population level Signals How vulnerable* is my control level today? Patient Health Score (Prognostic)
  36. 36. kHealth Bariatrics The purpose of our research is to determine if monitoring Bariatric patient’s pre- and postoperative compliance with active and passive sensors can bolster bariatric patient’s progress and lessen weight recidivism
  37. 37. ● 500 million people all over the world are obesity ● 36% of the adults in the United States suffer from obesity ● 65% of the world’s population lives in countries where the occurrence of death due to overweight and obesity is higher than being underweight Obesity
  38. 38. ● Chances of regaining weight as stomach can still expand after surgery ● Continuous monitoring of the patients by the surgeon is very essential Bariatric Surgery
  39. 39. Challenges Post-Bariatric Surgery ● Patient acceptance and active participation involving continuous monitoring of the patient ● Cost and reimbursement models ● Challenging research in understanding of variety of data over long period
  40. 40. A system that can ● monitor the patient continuously and remotely ● identify non-compliance before and after surgery ● nudge/assist for better compliance for improved outcomes and reduce recidivism Post-Bariatric Surgery Solution
  41. 41. kHealth Post-Bariatric Surgery Proposed Method Aggregate the data collected from the sensors, questionnaires and use artificial intelligence techniques to: ● analyse and predict the deviations that could cause the post surgical complications and, ● serve as an assistant leading to better patient-compliance and outcomes
  42. 42. kHealth Bariatrics: Kit
  43. 43. 43 43 How do we solve problems with real world complexity, gather vast amount of data, diverse knowledge……. and come up with intelligent decisions that works for an individual at a given time? next: a pedagogical take
  44. 44. Semantic Perceptual Cognitive computing
  45. 45. Interplay between Semantic, Cognitive and Perceptual Computing (SC, CC and PC) with examples Related videos, papers, slides via: http://knoesis.org/vision Semantic Perceptual Cognitive computing in two use cases: Asthma and Traffic Management
  46. 46. 46 Thank you Thank you, and please visit us at http://knoesis.org For more information on kHealth, please visit us at http://knoesis.org/projects/khealth Cognitive Computing Semantic Computing Perceptual Computing Contributors and collaborators for this talk: Pramod Anantharam Cory Henson Dr. T.K. Prasad Sanjaya Wijeratne Utkarshani Jaimini
  47. 47. Ohio Center of Excellence in Knowledge-enabled Computing Wright State University