Sana Tonekaboni

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden et al. · arXiv (Cornell University) · 2019 · 238 citations · Full text

Machine Learning, Clinical Specialties, Explainable Machine Learning +20

Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment

Hossein Kassiri, Sana Tonekaboni, Muhammad Tariqus Salam et al. · IEEE Transactions on Biomedical Circuits and Systems · 2017 · 114 citations

Medical Electronics, Engineering, Neuromodulation Therapies +31

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Sana Tonekaboni, Danny Eytan, Anna Goldenberg · arXiv (Cornell University) · 2021 · 32 citations · Full text

Engineering, Machine Learning, Data Science +14

What went wrong and when? Instance-wise feature importance for time-series black-box models

Sana Tonekaboni, Shalmali Joshi, Kieran R. Campbell et al. · Neural Information Processing Systems · 2020 · 27 citations

Forecasting Methodology, Engineering, Machine Learning +12

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.

Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden et al. · Machine Learning for Healthcare Conference · 2019 · 25 citations

Machine Learning, Clinical Specialties, Explainable Machine Learning +20