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ORCIDSPAC 2021: Palo Alto, CA, USA
- Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Proceedings of AAAI Symposium on Survival Prediction - Algorithms, Challenges and Applications, SPACA 2021, Stanford University, Palo Alto, CA, USA, March 22-24, 2021. Proceedings of Machine Learning Research 146, PMLR 2021 - Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Preface: AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021. 1-2 - Aziliz Cottin, Nicolas Pécuchet, Marine Zulian, Agathe Guilloux, Sandrine Katsahian:
IDNetwork: A deep Illness-Death Network based on multi-states event history process for versatile disease prognostication. 1-21 - David Hubbard, Benoit Rostykus, Yves Raimond, Tony Jebara:
Beta Survival Models. 22-39 - Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. 40-53 - Jonathan Heitz, Joanna Ficek, Martin Faltys
, Tobias M. Merz, Gunnar Rätsch, Matthias Hüser:
WRSE - a non-parametric weighted-resolution ensemble for predicting individual survival distributions in the ICU. 54-69 - Sunil Vasu Kalmady, Weijie Sun, Justin A. Ezekowitz, Nowell Fine, Jonathan Howlett, Anamaria Savu, Russ Greiner, Padma Kaul:
Improving the Calibration of Long Term Predictions of Heart Failure Rehospitalizations using Medical Concept Embedding. 70-82 - Ce Yang, Liqun Diao, Richard Cook:
Survival Trees for Current Status Data. 83-94 - Jeremy C. Weiss:
Wavelet Reconstruction Networks for Marked Point Processes. 95-106 - Peter Grünwald, Alexander Ly, Muriel Felipe Pérez-Ortiz, Judith Ter Schure:
The Safe Logrank Test: Error Control under Optional Stopping, Continuation and Prior Misspecification. 107-117 - Michael Sloma, Fayeq Syed, Mohammedreza Nemati, Kevin S. Xu:
Empirical Comparison of Continuous and Discrete-time Representations for Survival Prediction. 118-131 - Shi Hu, Egill A. Fridgeirsson, Guido van Wingen, Max Welling:
Transformer-Based Deep Survival Analysis. 132-148 - Donald K. K. Lee, Ningyuan Chen, Hemant Ishwaran, Xiaochen Wang, Arash Pakbin, Bobak Mortazavi, Hongyu Zhao:
Theory and software for boosted nonparametric hazard estimation. 149-158 - Preston Putzel, Padhraic Smyth, Jaehong Yu, Hua Zhong:
Dynamic Survival Analysis with Individualized Truncated Parametric Distributions. 159-170 - Xuejian Wang, Wenbin Zhang, Aishwarya Jadhav, Jeremy C. Weiss:
Harmonic-Mean Cox Models: A Ruler for Equal Attention to Risk. 171-183 - Chirag Nagpal, Vincent Jeanselme, Artur Dubrawski:
Deep Parametric Time-to-Event Regression with Time-Varying Covariates. 184-193 - Tristan Sylvain, Margaux Luck, Joseph Paul Cohen, Héloïse Cardinal, Andrea Lodi, Yoshua Bengio:
Exploring the Wasserstein metric for time-to-event analysis. 194-206 - Aliasghar Tarkhan, Noah Simon, Thomas Bengtsson, Trung Kien Nguyen, Jian Dai:
Survival Prediction Using Deep Learning. 207-214 - Prasad Bankar, Subhasis Panda, Vaibhav Anand, Vineet Kumar:
Risk and Survival Analysis from COVID Outbreak Data: Lessons from India. 215-222 - Sejin Kim, Michal Kazmierski, Benjamin Haibe-Kains:
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks. 223-231 - Di Wang, Wen Ye, Kevin He:
Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset. 232-239 - Li-Hao Kuan, Russell Greiner:
Finding Relevant Features for Different Times in Survival Prediction by Discrete Hazard Bayesian Network. 240-251 - Yan Gao, Yan Cui:
Multi-ethnic Survival Analysis: Transfer Learning with Cox Neural Networks. 252-257
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