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Machine Learning for Mobile Health

Joseph Futoma · Walter Dempsey · Katherine Heller · Yian Ma · Nicholas Foti · Marianne Njifon · Kelly Zhang · Jieru Shi

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.

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Timezone: America/Los_Angeles