Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease
Publication
, Journal Article
Futoma, J; Sendak, M; Cameron, B; Heller, K
Published in: Uncertainty in Artificial Intelligence 2016 Proceedings
June 29, 2016
Duke Scholars
Published In
Uncertainty in Artificial Intelligence 2016 Proceedings
Publication Date
June 29, 2016
Citation
APA
Chicago
ICMJE
MLA
NLM
Futoma, J., Sendak, M., Cameron, B., & Heller, K. (2016). Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease. Uncertainty in Artificial Intelligence 2016 Proceedings.
Futoma, J., M. Sendak, B. Cameron, and K. Heller. “Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease.” Uncertainty in Artificial Intelligence 2016 Proceedings, June 29, 2016.
Futoma J, Sendak M, Cameron B, Heller K. Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease. Uncertainty in Artificial Intelligence 2016 Proceedings. 2016 Jun 29;
Futoma, J., et al. “Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease.” Uncertainty in Artificial Intelligence 2016 Proceedings, June 2016.
Futoma J, Sendak M, Cameron B, Heller K. Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease. Uncertainty in Artificial Intelligence 2016 Proceedings. 2016 Jun 29;
Published In
Uncertainty in Artificial Intelligence 2016 Proceedings
Publication Date
June 29, 2016