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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]

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Intae Moon

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Stefan Groha

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Alexander Gusev

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