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abstract

Physical analytics to model health behaviors

Published: 11 June 2014 Publication History

Abstract

Mobile phones are a pervasive platform for opportunistic sensing of social and health related behaviors. In this talk, I discuss how sensor data from mobile phones can be used to model and predict health outcomes. The talk starts with a review of research at the MIT Media Lab, and then transitions into how Ginger.io has built a commercial platform to collect, annotate, analyze and drive healthcare interventions at scale, deployed with major US hospital systems and healthcare providers. The Ginger.io three-part platform -- patient app, behavioral analytics engine, and provider dashboard -- applies this technology to give care providers a window into their patients' health between office visits. Our mobile app uses smartphone sensors to passively collect information about a patient's daily patterns. Using this data, our machine learning models are able to detect at-risk patients significantly better than the standard of care. Any concerning changes in behavior are communicated to the provider through our simple, action-oriented web dashboard. Ginger.io is part of the care solutions at institutions such as Kaiser Permanente, Novant Health, UCSF, Duke Medical and Cincinnati Children's.

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cover image ACM Conferences
WPA '14: Proceedings of the 2014 workshop on physical analytics
June 2014
54 pages
ISBN:9781450328258
DOI:10.1145/2611264
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 11 June 2014

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Author Tags

  1. applications
  2. health
  3. machine learning

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MobiSys'14
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WPA '14 Paper Acceptance Rate 6 of 8 submissions, 75%;
Overall Acceptance Rate 11 of 17 submissions, 65%

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