Sana Tonekaboni
Sana Tonekaboni
University of Toronto, Vector Institute
Verified email at - Homepage
Cited by
Cited by
What clinicians want: contextualizing explainable machine learning for clinical end use
S Tonekaboni, S Joshi, MD McCradden, A Goldenberg
Machine Learning for Healthcare Conference, 359-380, 2019
Closed-loop neurostimulators: A survey and a seizure-predicting design example for intractable epilepsy treatment
H Kassiri, S Tonekaboni, MT Salam, N Soltani, K Abdelhalim, ...
IEEE transactions on biomedical circuits and systems 11 (5), 1026-1040, 2017
Unsupervised representation learning for time series with temporal neighborhood coding
S Tonekaboni, D Eytan, A Goldenberg
International Conference on Learning Representations, 2021
What went wrong and when? Instance-wise feature importance for time-series black-box models
S Tonekaboni, S Joshi, K Campbell, DK Duvenaud, A Goldenberg
Advances in Neural Information Processing Systems 33, 2020
Prediction of cardiac arrest from physiological signals in the pediatric ICU
S Tonekaboni, M Mazwi, P Laussen, D Eytan, R Greer, SD Goodfellow, ...
Machine Learning for Healthcare Conference, 534-550, 2018
Explaining time series by counterfactuals
S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg
Decoupling local and global representations of time series
S Tonekaboni, CL Li, SO Arik, A Goldenberg, T Pfister
International Conference on Artificial Intelligence and Statistics, 8700-8714, 2022
How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU
S Tonekaboni, G Morgenshtern, A Assadi, A Pokhrel, X Huang, ...
Conference on Health, Inference, and Learning, 169-182, 2022
Learning Unsupervised Representations for ICU Timeseries
A Weatherhead, R Greer, MA Moga, M Mazwi, D Eytan, A Goldenberg, ...
Conference on Health, Inference, and Learning, 152-168, 2022
Time-Varying Correlation Networks for Interpretable Change Point Detection
K Garg, S Tonekaboni, A Goldenberg
arXiv preprint arXiv:2211.03991, 2022
Modeling Heart Rate Response to Exercise with Wearables Data
A Nazaret, S Tonekaboni, G Darnell, S Ren, G Sapiro, A Miller
NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022
The system can't perform the operation now. Try again later.
Articles 1–11