Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Wu Z, Yang Y, Fasching PA, Tresp V (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: ML Research Press
Book Volume: 149
Pages Range: 54-79
Conference Proceedings Title: Proceedings of Machine Learning Research
Event location: Virtual, Online
Abstract
Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy and neglect prediction uncertainty. We propose Deep Kernel Accelerated Failure Time models for the time-to-event prediction task, enabling uncertainty-awareness of the prediction by a pipeline of a recurrent neural network and a sparse Gaussian Process. Furthermore, a deep metric learning based pre-training step is adapted to enhance the proposed model. Our model shows better point estimate performance than recurrent neural network based baselines in experiments on two real-world datasets. More importantly, the predictive variance from our model can be used to quantify the uncertainty estimates of the time-to-event prediction: Our model delivers better performance when it is more confident in its prediction. Compared to related methods, such as Monte Carlo Dropout, our model offers better uncertainty estimates by leveraging an analytical solution and is more computationally efficient.
Involved external institutions
Germany (DE)How to cite
APA:
Wu, Z., Yang, Y., Fasching, P.A., & Tresp, V. (2021). Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models. In Ken Jung, Serena Yeung, Mark Sendak, Michael Sjoding, Rajesh Ranganath (Eds.), Proceedings of Machine Learning Research (pp. 54-79). Virtual, Online: ML Research Press.
MLA:
Wu, Zhiliang, et al. "Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models." Proceedings of the 6th Machine Learning for Healthcare Conference, MLHC 2021, Virtual, Online Ed. Ken Jung, Serena Yeung, Mark Sendak, Michael Sjoding, Rajesh Ranganath, ML Research Press, 2021. 54-79.
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