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Foryciarz, Agata
17  results:
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1

Clinical utility gains from incorporating comorbidity and g..:

Xu, Yizhe ; Foryciarz, Agata ; Steinberg, Ethan.
Journal of the American Medical Informatics Association.  30 (2023)  5 - p. 878-887 , 2023
Link: https://doi.org/10.1093/..
 
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2

Net benefit, calibration, threshold selection, and training..:

, In: 2022 ACM Conference on Fairness, Accountability, and Transparency,
Pfohl, Stephen ; Xu, Yizhe ; Foryciarz, Agata... - p. 1039-1052 , 2022
Link: https://dl.acm.org/doi/1..
 
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3

A comparison of approaches to improve worst-case predictive..:

Pfohl, Stephen R. ; Zhang, Haoran ; Xu, Yizhe...
Scientific Reports.  12 (2022)  1 - p. , 2022
Link: https://doi.org/10.1038/..
 
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4

Evaluating algorithmic fairness in the presence of clinical..:

Foryciarz, Agata ; Pfohl, Stephen R ; Patel, Birju.
BMJ Health & Care Informatics.  29 (2022)  1 - p. e100460 , 2022
Link: https://doi.org/10.1136/..
 
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5

An empirical characterization of fair machine learning for ..:

Pfohl, Stephen R. ; Foryciarz, Agata ; Shah, Nigam H.
Journal of Biomedical Informatics.  113 (2021)  - p. 103621 , 2021
Link: https://doi.org/10.1016/..
 
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6

Where do algorithmic accountability and explainability fram..:

, In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency,
Szymielewicz, Katarzyna ; Bacciarelli, Anna ; Hidvegi, Fanny... - p. 689 ff. , 2020
Link: https://dl.acm.org/doi/1..
 
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7

Clinical utility gains from incorporating comorbidity and g..:

Xu, Yizhe ; Foryciarz, Agata ; Steinberg, Ethan.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114071/.  , 2023
Link: http://www.ncbi.nlm.nih...
 
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8

Clinical Utility Gains from Incorporating Comorbidity and G..:

Xu, Yizhe ; Foryciarz, Agata ; Steinberg, Ethan.
http://arxiv.org/abs/2209.06985.  , 2022
Link: http://arxiv.org/abs/220..
 
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9

A comparison of approaches to improve worst-case predictive..:

Pfohl, Stephen R ; Zhang, Haoran ; Xu, Yizhe...
10.1038/S41598-022-07167-7.  , 2022
Link: https://hdl.handle.net/1..
 
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10

Net benefit, calibration, threshold selection, and training..:

Pfohl, Stephen R ; Xu, Yizhe ; Foryciarz, Agata...
http://arxiv.org/abs/2202.01906.  , 2022
Link: http://arxiv.org/abs/220..
 
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11

A comparison of approaches to improve worst-case predictive..:

Pfohl, Stephen R ; Zhang, Haoran ; Xu, Yizhe...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8885701/.  , 2022
Link: http://www.ncbi.nlm.nih...
 
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12

Evaluating algorithmic fairness in the presence of clinical..:

Foryciarz, Agata ; Pfohl, Stephen R ; Patel, Birju.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996004/.  , 2022
Link: http://www.ncbi.nlm.nih...
 
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13

A comparison of approaches to improve worst-case predictive..:

Pfohl, Stephen R ; Zhang, Haoran ; Xu, Yizhe...
http://arxiv.org/abs/2108.12250.  , 2021
Link: http://arxiv.org/abs/210..
 
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14

Zero-Shot Transfer Learning with Synthesized Data for Multi..:

Campagna, Giovanni ; Foryciarz, Agata ; Moradshahi, Mehrad.
http://arxiv.org/abs/2005.00891.  , 2020
Link: http://arxiv.org/abs/200..
 
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15

An Empirical Characterization of Fair Machine Learning For ..:

Pfohl, Stephen R ; Foryciarz, Agata ; Shah, Nigam H
http://arxiv.org/abs/2007.10306.  , 2020
Link: http://arxiv.org/abs/200..
 
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