SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction

Intae Moon, Stefan Groha, Alexander Gusev. SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction. In Zachary C. Lipton, Rajesh Ranganath, Mark P. Sendak, Michael W. Sjoding, Serena Yeung, editors, Proceedings of the Machine Learning for Healthcare Conference, MLHC 2022, 5-6 August 2022, Durham, NC, USA. Volume 182 of Proceedings of Machine Learning Research, pages 800-827, PMLR, 2022. [doi]