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Marzyeh Ghassemi
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1

Time2Stop: Adaptive and Explainable Human-AI Loop for Smart..:

, In: Proceedings of the CHI Conference on Human Factors in Computing Systems,
Orzikulova, Adiba ; Xiao, Han ; Li, Zhipeng... - p. 1-20 , 2024
Link: https://dl.acm.org/doi/1..
 
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2

Taking Off with AI: Lessons from Aviation for Healthcare:

, In: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization,
Bondi-Kelly, Elizabeth ; Hartvigsen, Tom ; Sanneman, Lindsay M... - p. 1-14 , 2023
Link: https://dl.acm.org/doi/1..
 
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3

Foundation Models in Healthcare: Opportunities, Risks & Str..:

, In: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems,
Thieme, Anja ; Nori, Aditya ; Ghassemi, Marzyeh... - p. 1-4 , 2023
Link: https://dl.acm.org/doi/1..
 
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4

Evaluating the Impact of Social Determinants on Health Pred..:

, In: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society,
Yang, Ming Ying ; Kwak, Gloria Hyunjung ; Pollard, Tom.. - p. 333-350 , 2023
Link: https://dl.acm.org/doi/1..
 
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5

In the Name of Fairness: Assessing the Bias in Clinical Rec..:

, In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency,
Xiao, Yuxin ; Lim, Shulammite ; Pollard, Tom Joseph. - p. 123-137 , 2023
Link: https://dl.acm.org/doi/1..
 
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6

Uniform Priors for Data-Efficient Learning:

, In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),
Sinha, Samarth ; Roth, Karsten ; Goyal, Anirudh... - p. 4016-4027 , 2022
Link: https://doi.org/10.1109/..
 
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7

Get To The Point! Problem-Based Curated Data Views To Augme..:

, In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems,
Zhang, Minfan ; Ehrmann, Daniel ; Mazwi, Mjaye... - p. 1-13 , 2022
Link: https://dl.acm.org/doi/1..
 
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8

The Road to Explainability is Paved with Bias: Measuring th..:

, In: 2022 ACM Conference on Fairness, Accountability, and Transparency,
Balagopalan, Aparna ; Zhang, Haoran ; Hamidieh, Kimia... - p. 1194-1206 , 2022
Link: https://dl.acm.org/doi/1..
 
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9

Write It Like You See It: Detectable Differences in Clinica..:

, In: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society,
Adam, Hammaad ; Yang, Ming Ying ; Cato, Kenrick... - p. 7-21 , 2022
Link: https://dl.acm.org/doi/1..
 
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10

Can You Fake It Until You Make It? : Impacts of Differen..:

, In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency,
Cheng, Victoria ; Suriyakumar, Vinith M. ; Dullerud, Natalie.. - p. 149-160 , 2021
Link: https://dl.acm.org/doi/1..
 
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11

An empirical framework for domain generalization in clinica..:

, In: Proceedings of the Conference on Health, Inference, and Learning,
Zhang, Haoran ; Dullerud, Natalie ; Seyyed-Kalantari, Laleh... - p. 279-290 , 2021
Link: https://dl.acm.org/doi/1..
 
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12

Chasing Your Long Tails : Differentially Private Predict..:

, In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency,
Suriyakumar, Vinith M. ; Papernot, Nicolas ; Goldenberg, Anna. - p. 723-734 , 2021
Link: https://dl.acm.org/doi/1..
 
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13

A comprehensive EHR timeseries pre-training benchmark:

, In: Proceedings of the Conference on Health, Inference, and Learning,
McDermott, Matthew ; Nestor, Bret ; Kim, Evan... - p. 257-278 , 2021
Link: https://dl.acm.org/doi/1..
 
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14

Pulling Up by the Causal Bootstraps : Causal Data Augmen..:

, In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management,
Gowda, Sindhu C. M. ; Joshi, Shalmali ; Zhang, Haoran. - p. 606-616 , 2021
Link: https://dl.acm.org/doi/1..
 
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15

MIMIC-Extract : a data extraction, preprocessing, and re..:

, In: Proceedings of the ACM Conference on Health, Inference, and Learning,
Wang, Shirly ; McDermott, Matthew B. A. ; Chauhan, Geeticka... - p. 222-235 , 2020
Link: https://dl.acm.org/doi/1..
 
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