BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data

no code implementations • 12 Sep 2022 • Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric Hekler, Misha Pavel, Daniel Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin

In this paper we present BayesLDM, a system for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model.

Bayesian Inference Time Series +1

Transfer Learning for Activity Recognition in Mobile Health

1 code implementation • 12 Jul 2020 • Yuchao Ma, Andrew T. Campbell, Diane J. Cook, John Lach, Shwetak N. Patel, Thomas Ploetz, Majid Sarrafzadeh, Donna Spruijt-Metz, Hassan Ghasemzadeh

While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.

Activity Recognition Transfer Learning

Estimating Individualized Treatment Regimes from Crossover Designs

no code implementations • 5 Feb 2019 • Crystal T. Nguyen, Daniel J. Luckett, Anna R. Kahkoska, Grace E. Shearrer, Donna Spruijt-Metz, Jaimie N. Davis, Michael R. Kosorok

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way.