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

1. Large Scale Health Telemetry and Analytics with
MQTT, Hadoop and Machine Learning DSLs
Murali Kaundinya & Gopinath Janakiraman
July 11, 2016

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
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3. 3
Murali Kaundinya
Applied Technology @ Merck
Gopinath Janakiraman
Applied Technology @ Merck

4. Acknowledge contributions from …
• Jakub Kotowski
• Semion Andreevich Piskarev
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5. 5
Overview

6. Digital Health Trends
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AccelerationofDigitalTechnology
Wireless
Sensors
Mobile
Connectivity
Social
Networking
Genomics
Internet
Imaging
Data Universe
Health Information
Systems
Disease
Diagnosis
Management
Prevention
Prediction
Time

7. Digital Health – Trends
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8. Predictive Analytics can identify at-risk patients
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Study found 55% of patients predicted as “highest risk” were admitted within 6 months

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.
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10. Technologies in scope
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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
2009 Flu Pandemic

12. 12
Demos

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
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14. Telemetry and Visualization with DSL - Demo
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15. Regression/Confidence with Allergies - Demo
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16. Predictive Analytics with DSLs- Demo
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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
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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
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19. Next Steps
• Welcome community development!
• Murali.Kaundinya@merck.com
• janakiraman_gopinath@merck.com
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