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Large Scale Health Telemetry and Analytics with MQTT, Hadoop and Machine Learning DSLs

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Large Scale Health Telemetry and Analytics with MQTT, Hadoop and Machine Learning DSLs

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Large Scale Health Telemetry and Analytics with MQTT, Hadoop and Machine Learning DSLs

  1. 1. Large Scale Health Telemetry and Analytics with MQTT, Hadoop and Machine Learning DSLs Murali Kaundinya & Gopinath Janakiraman July 11, 2016
  2. 2. Agenda • Overview • Technologies in scope • Platform Architecture – Context for demos • Telemetry & Visualization with DSL (Demo) • Regression/Confidence on Allergies with DSL (Demo) • Predictive Analytics with DSL (Demo) • Summary 2
  3. 3. 3 Murali Kaundinya Applied Technology @ Merck Gopinath Janakiraman Applied Technology @ Merck
  4. 4. Acknowledge contributions from … • Jakub Kotowski • Semion Andreevich Piskarev 4
  5. 5. 5 Overview
  6. 6. Digital Health Trends 6 AccelerationofDigitalTechnology Wireless Sensors Mobile Connectivity Social Networking Genomics Internet Imaging Data Universe Health Information Systems Disease Diagnosis Management Prevention Prediction Time
  7. 7. Digital Health – Trends 7
  8. 8. Predictive Analytics can identify at-risk patients 8 Study found 55% of patients predicted as “highest risk” were admitted within 6 months
  9. 9. Opportunities with Healthcare Wearables • Devices that drive better outcomes will thrive. • The KPIs are: – Increasing quality of care. – Lowering cost. – Decreasing hospitalizations. • Chronic diseases can benefit the most. • FDA regulation increases reliability and quality. 9
  10. 10. Technologies in scope 10 Sensors Devices Wearables PHRs EMRs Aggregators/DataStreams Ingestion Stratification Analytics Querying/Visualization/APIs DSLs Meta-Programming System M2Ms WEKA HBase Patient Portal Provider Portal Payer Portal Care Coordinator Portal
  11. 11. 11 2009 Flu Pandemic
  12. 12. 12 Demos
  13. 13. Wearable Devices • Experiences with – Fitbit – Misfit – Apple’s Research Kit – Google Fit – Microsoft Band • Experience with device portals – Validic, Data Minded Solutions, Human API, REDOX 13
  14. 14. Telemetry and Visualization with DSL - Demo 14
  15. 15. Regression/Confidence with Allergies - Demo 15
  16. 16. Predictive Analytics with DSLs- Demo 16
  17. 17. In closing … • IoT – Telemetry - Easier to embed, integrate – More devices, generating more non-standard data. • Discoverable data sources (internal and external) – Machine toolable • Domain Specific Languages – Declarative programming w/ projectional editors • Abstract away complexity – Compute, Analytics/Machine Learning • Visualize data 17
  18. 18. What’s next… • Internet of Medical Things – More devices, generating more non-standard data. • Consent and sharing – Privacy, Compliance • Interoperability with EMRs – FHIR • Precision Medicine – Genomic Sequencing, Personalized Medicine • Population Health – Longitudinal data++, disease models, preventive care 18
  19. 19. Next Steps • Welcome community development! • Murali.Kaundinya@merck.com • janakiraman_gopinath@merck.com 19

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