Small, n=me, data

Deborah EstrinProfessor, Computer Science, Cornell NYC Tech

Professor, Public Health, Weill Cornell Medical CollegeCo-founder, Open mHealth

destrin@cs.cornell.eduwork done with collaborators from

Cornell, UCLA, openmhealth.org, ...

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1Saturday, August 17, 13

Agenda

• Shared, digital, life

• Prelude: mobile health as n=me data

• Small data: beyond mobile and beyond health

• Proposed market/system architecture with individual as nexus of control

• Proposed shared testbed

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n=medata

third pillar of personalized, precision, medicine“Big data”

(EHRs, Web mining)

“n=me data” (mHealth,

digital traces)+ +“omics”

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mobile apps generate data

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Passively-recorded activity and location traces

UI: E. Wang

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h"p://ginger.io/the-­‐pla1orm/

Communication and activity data: Ginger.io check engine light

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• mobile carriers- location/activity - call records

• cable box/home gw- TV patterns (sleep,hearing)- internet mediated patterns- household focus

• utilities (elec,water)- diurnal rhythms- appliance use- household focus

• smart cars- location/activity

• search- state of mind- topic/concern

• social media and email- social patterns- interaction- mood

• e-commerce, payments- consumption/input- patterns

• games/music/videos- cognitive state- indicator/influencer

Beyond mobilesmall data: digital traces from diverse consumer services

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measure, manage, incentivize, improve: wellbeing, consumption, personal/family logistics behavior change, community resiliency

• aging independent living seniors

• newly independent living young adults

• newcomers to a neighborhood/city

• personal profiles in social media, games

• n=me health and wellness outcomes

• ...

Beyond ‘health’...life:

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Proposed small data socio-technical architecture

individual as nexus for fusion of their data streamsapps run over data in personal data vault

• Subscriber access to their individual data traces--data liberation!

•programmatic, realtime, opt-in through personal data APIs

• Raw data shared with subscriber only

•avoids a range of privacy and regulatory concerns

• Fuel new market of third-party personal informatics apps/services

• some apps will run in PDV; others externally

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Individual as nexus: discussion

• Each data source has shared/other origins

• Individual has control over their corpus of data streams to correlate, fuse

• App/service utility derives from lack of anonymity

• Selective sharing embodied in apps

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small data: key challenges

• Getting the data– Personal data APIs (data liberation to the consumer)– Convincing/incentivizing service providers

• Data processing, inference, fusion, modeling– diverse, noisy, lossy data– signal processing, machine learning, natural language...

• Data and API standards– app model and economy

• Personal data vaults– Security models and mechanisms, usability– Policy questions re. ownership, access, rights

• Testbed for prototypes and pilots– economy of scale in a shared testbed for rapid iterative exploration– secure and private data handling, IRB, methods, tools 11

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mpireproposed Testbed for Small Data and personal informatics

participant recruitment, incentives, management--across large and diverse participant populations

experiment configuration, control, coordination, analysis, administration

IRB study management templates

aggregated datasets

Current collaborators: Intel Labs, Ericsson Research, IBM Research, ATT Research, Verizon, Time Warner Cable

Network and cloud APIs to personal-data

Deployment framework for end-user apps

Secure personal-data vaults

Data processing, fusion, inference, modeling12

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open architecture to promote modularity, data interoperability and software reusability

Can we reuse open mhealth architecture/APIs/Registry?

activity classification

graphing significant changes in mobility

data storage (e.g., PHR)

proprietary component with omh API (RunkeeperEntra Glucometer)

mood App (PAM, moodmap...)

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http://smalldata.tech.cornell.edu/

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