Shalmali Joshi

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Dr. Marzyeh GhassemiAssociate Professor, EECS/IMES, MITVerified email at mit.edu
Melissa McCraddenAustralian Institute for Machine learningVerified email at adelaide.edu.au
Irene Y. ChenAssistant Professor, UC Berkeley and UC San FranciscoVerified email at berkeley.edu
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of TorontoVerified email at utoronto.ca
Sana TonekaboniBroad Institute of MIT and HarvardVerified email at broadinstitute.org
Joydeep Ghosh(Chaired) Professor, ECE Dept., Univ. Texas at Austin; Faculty Dell Med, UT-Comp. Sc., McCombsVerified email at utexas.edu
Sanmi KoyejoAssistant Professor, Stanford UniversityVerified email at stanford.edu
Emma PiersonCornell TechVerified email at cornell.edu
Kadija FerrymanBerman Institute of Bioethics and Department of Health Policy and Management, Bloomberg School ofVerified email at jhu.edu
Sohini UpadhyayHarvard UniversityVerified email at g.harvard.edu
Haoran ZhangPhD Student, Massachusetts Institute of TechnologyVerified email at mit.edu
Been KimGoogle DeepMindVerified email at csail.mit.edu
Warut VijitbenjaronkUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
Randi Zlotnik ShaulDirector Bioethics Department, The Hospital for Sick ChildrenVerified email at sickkids.ca
Kieran R CampbellTorontoVerified email at lunenfeld.ca
Rajesh RanganathAssistant Professor, NYUVerified email at cs.princeton.edu
Quaid MorrisComputational and Systems Biology Program, Memorial Sloan Kettering Cancer CenterVerified email at mskcc.org
Vinith M. SuriyakumarMassachusetts Institute of TechnologyVerified email at mit.edu

Shalmali Joshi

Columbia University

Verified email at cumc.columbia.edu - Homepage

Machine Learning Healthcare


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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	494	2019
Ethical machine learning in healthcare IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi Annual review of biomedical data science 4 (1), 123-144, 2021	453	2021
Ethical limitations of algorithmic fairness solutions in health care machine learning MD McCradden, S Joshi, M Mazwi, JA Anderson The Lancet Digital Health 2 (5), e221-e223, 2020	195	2020
Towards realistic individual recourse and actionable explanations in black-box decision making systems S Joshi, O Koyejo, W Vijitbenjaronk, B Kim, J Ghosh arXiv preprint arXiv:1907.09615, 2019	185	2019
Treating health disparities with artificial intelligence IY Chen, S Joshi, M Ghassemi Nature medicine 26 (1), 16-17, 2020	118	2020
Towards robust and reliable algorithmic recourse S Upadhyay, S Joshi, H Lakkaraju Advances in Neural Information Processing Systems 34, 16926-16937, 2021	104	2021
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning MD McCradden, S Joshi, JA Anderson, M Mazwi, A Goldenberg, ... Journal of the American Medical Informatics Association 27 (12), 2024-2027, 2020	91	2020
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, 799-809, 2020	82	2020
Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis M Pawelczyk, C Agarwal, S Joshi, S Upadhyay, H Lakkaraju International Conference on Artificial Intelligence and Statistics, 4574-4594, 2022	70*	2022
Probabilistic machine learning for healthcare IY Chen, S Joshi, M Ghassemi, R Ranganath Annual review of biomedical data science 4 (1), 393-415, 2021	70	2021
An empirical framework for domain generalization in clinical settings H Zhang, N Dullerud, L Seyyed-Kalantari, Q Morris, S Joshi, M Ghassemi Proceedings of the conference on health, inference, and learning, 279-290, 2021	66	2021
Can you fake it until you make it? impacts of differentially private synthetic data on downstream classification fairness V Cheng, VM Suriyakumar, N Dullerud, S Joshi, M Ghassemi Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021	53	2021
xGEMs: Generating Examplars to Explain Black-Box Models S Joshi, O Koyejo, B Kim, J Ghosh https://arxiv.org/abs/1806.08867, 2018	44	2018
Identifiable phenotyping using constrained non-negative matrix factorization S Joshi, S Gunasekar, D Sontag, G Joydeep Machine Learning for Healthcare Conference, 17-41, 2016	30	2016
Making machine learning matter to clinicians: model actionability in medical decision-making DE Ehrmann, S Joshi, SD Goodfellow, ML Mazwi, D Eytan NPJ Digital Medicine 6 (1), 7, 2023	23	2023
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty S Joshi, S Parbhoo, F Doshi-Velez arXiv preprint arXiv:2109.06312, 2021	19	2021
" why did the model fail?": Attributing model performance changes to distribution shifts H Zhang, H Singh, M Ghassemi, S Joshi	17	2023
Sequential explanations with mental model-based policies A Yeung, S Joshi, JJ Williams, F Rudzicz arXiv preprint arXiv:2007.09028, 2020	16	2020
What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB M Mccradden, O Odusi, S Joshi, I Akrout, K Ndlovu, B Glocker, G Maicas, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023	13	2023
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.’ArXiv 2019 S Tonekaboni, S Joshi, MD McCradden, A Goldenberg arXiv preprint arXiv:1905.05134, 1905	13	1905

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