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iSurvive: An Interpretable, Event-time Prediction Model for mHealth
Walter Dempsey · Alexander Moreno · James Rehg · Susan Murphy · Chris Scott · Michael Dennis · David Gustafson

Wed Aug 09 01:30 AM -- 05:00 AM (PDT) @ Gallery #52

An important mobile health (mHealth) task is the use of multimodal data, such as sensor streams and self-report, to construct interpretable time-to-event predictions of, for example, lapse to alcohol or illicit drug use. Interpretability of the prediction model is important for acceptance and adoption by domain scientists, enabling model outputs and parameters to inform theory and guide intervention design. Temporal latent state models are therefore attractive, and so we adopt the continuous time hidden Markov model (CT-HMM) due to its ability to describe irregular arrival times of event data. Standard CT-HMMs, however, are not specialized for predicting the time to a future event, the key variable for mHealth interventions. Also, standard emission models lack a sufficiently rich structure to describe multimodal data and incorporate domain knowledge. We present iSurvive, an extension of classical survival analysis to a CT-HMM. We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.

Author Information

Walter Dempsey (University of Michigan)
Alexander Moreno (Georgia Institute of Technology)
James Rehg (Georgia Tech)
Susan Murphy (University of Michigan)
Chris Scott (Chestnut Health Systems)
Michael Dennis (Lighthouse Institute)
David Gustafson (University of Wisconsin-Madison)

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