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Workshop
Sat Dec 12 07:00 AM -- 02:30 PM (PST)
Machine Learning for Mobile Health
Joseph Futoma · Walter Dempsey · Katherine Heller · Yian Ma · Nicholas Foti · Marianne Njifon · Kelly Zhang · Jieru Shi





Workshop Home Page

Mobile health (mHealth) technologies have transformed the mode and quality of clinical research. Wearable sensors and mobile phones provide real-time data streams that support automated clinical decision making, allowing researchers and clinicians to provide ecological and in-the-moment support to individuals in need. Mobile health technologies are used across various health fields. Their inclusion in clinical care has aimed to improve HIV medication adherence, to increase activity, supplement counseling/pharmacotherapy in treatment for substance use, reinforce abstinence in addictions, and to support recovery from alcohol dependence. The development of mobile health technologies, however, has progressed at a faster pace than the science and methodology to evaluate their validity and efficacy.


Current mHealth technologies are limited in their ability to understand how adverse health behaviors develop, how to predict them, and how to encourage healthy behaviors. In order for mHealth to progress and have expanded impact, the field needs to facilitate collaboration among machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians. Techniques from multiple fields can be brought to bear on the substantive problems facing this interdisciplinary discipline: experimental design, causal inference, multi-modal complex data analytics, representation learning, reinforcement learning, deep learning, transfer learning, data visualization, and clinical integration.

This workshop will assemble researchers from the key areas in this interdisciplinary space necessary to better address the challenges currently facing the widespread use of mobile health technologies.

Intro
Invited Talk: Matthew Nock (Invited Talk)
Invited Talk: Lee Hartsell (Invited Talk)
Invited Talk: AI for Decision Support in Low Resource Areas (Invited Talk)
Using Wearables for Influenza-Like Illness Detection: The importance of design (Spotlight Talk)
Representing and Denoising Wearable ECG Recordings (Spotlight Talk)
Towards Personal Hand Hygiene Detection in Free-living Using Wearable Devices (Spotlight Talk)
Q&A for Morning Spotlight Talks (Q&A in Gather.Town)
Discussion for Invited Speakers: Matthew Nock, Lee Hartsell, Ally Salim Jr (Discussion Panel in Gather.Town)
Poster Session in Gather Town (Poster Session)
Lunch / Networking Break (Lunch)
Invited Talk: Assessing Personalization in Digital Health (Invited Talk)
Invited Talk: Tanzeem Choudhury (Invited Talk)
Invited Talk: Language-based Behavior and Interventions in Mobile Health (Invited Talk)
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data (Spotlight Talk)
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data (Spotlight Talk)
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health (Spotlight Talk)
Q&A for Afternoon Spotlight Talks (Q&A in Gather.Town)
Discussion with Invited Speakers: Susan Murphy, Tanzeem Choudhury, Tim Althoff (Discussion Panel)
Poster Session in Gather Town (Poster Session)
Concluding Remarks (in Gather.Town) (Conclusion)