iSurvive: An Interpretable, Event-time Prediction Model for mHealth

ICML 2017 Walter H. DempseyAlexander MorenoChristy K. ScottMichael L. DennisDavid H. GustafsonSusan A. MurphyJames M. Rehg

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... (read more)

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