Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Pfohl, Stephen R"'
Autor:
Pfohl, Stephen R., Cole-Lewis, Heather, Sayres, Rory, Neal, Darlene, Asiedu, Mercy, Dieng, Awa, Tomasev, Nenad, Rashid, Qazi Mamunur, Azizi, Shekoofeh, Rostamzadeh, Negar, McCoy, Liam G., Celi, Leo Anthony, Liu, Yun, Schaekermann, Mike, Walton, Alanna, Parrish, Alicia, Nagpal, Chirag, Singh, Preeti, Dewitt, Akeiylah, Mansfield, Philip, Prakash, Sushant, Heller, Katherine, Karthikesalingam, Alan, Semturs, Christopher, Barral, Joelle, Corrado, Greg, Matias, Yossi, Smith-Loud, Jamila, Horn, Ivor, Singhal, Karan
Publikováno v:
Nature Medicine (2024)
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developin
Externí odkaz:
http://arxiv.org/abs/2403.12025
Autor:
Tsai, Katherine, Pfohl, Stephen R., Salaudeen, Olawale, Chiou, Nicole, Kusner, Matt J., D'Amour, Alexander, Koyejo, Sanmi, Gretton, Arthur
We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels. In this setting, neither the covariate shi
Externí odkaz:
http://arxiv.org/abs/2403.07442
Autor:
Alabdulmohsin, Ibrahim, Chiou, Nicole, D'Amour, Alexander, Gretton, Arthur, Koyejo, Sanmi, Kusner, Matt J., Pfohl, Stephen R., Salaudeen, Olawale, Schrouff, Jessica, Tsai, Katherine
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor la
Externí odkaz:
http://arxiv.org/abs/2212.11254
Autor:
Alderman, Joseph E a, b, i, †, Palmer, Joanne a, b, i, †, Laws, Elinor a, b, i, †, McCradden, Melissa D c, d, Ordish, Johan i, j, m, Ghassemi, Marzyeh o, p, Pfohl, Stephen R q, Rostamzadeh, Negar r, Cole-Lewis, Heather s, Glocker, Ben t, Calvert, Melanie a, b, e, f, g, Pollard, Tom J p, Gill, Jaspret a, Gath, Jacqui u, v, Adebajo, Adewale x, Beng, Jude y, Leung, Cassandra H w, Kuku, Stephanie z, Farmer, Lesley-Anne ac, Matin, Rubeta N ad, ae, Mateen, Bilal A aa, af, ag, McKay, Francis ah, ai, Heller, Katherine q, Karthikesalingam, Alan aj, Treanor, Darren ak, al, am, Mackintosh, Maxine an, ao, Oakden-Rayner, Lauren ap, Pearson, Russell aq, Manrai, Arjun K ar, Myles, Puja aq, Kumuthini, Judit as, Kapacee, Zoher at, Sebire, Neil J ab, Nazer, Lama H au, Seah, Jarrel av, aw, ax, Akbari, Ashley ay, Berman, Lew az, Gichoya, Judy W ba, Righetto, Lorenzo bb, Samuel, Diana bc, Wasswa, William bd, Charalambides, Maria be, bg, Arora, Anmol k, n, Pujari, Sameer bh, Summers, Charlotte l, Sapey, Elizabeth a, b, h, i, bi, bj, bk, Wilkinson, Sharon bf, bl, Thakker, Vishal bm, Denniston, Alastair a, b, e, h, i, bn, Liu, Xiaoxuan a, b, h, i, *
Publikováno v:
In The Lancet Digital Health January 2025 7(1):e64-e88
Autor:
Pfohl, Stephen R., Xu, Yizhe, Foryciarz, Agata, Ignatiadis, Nikolaos, Genkins, Julian, Shah, Nigam H.
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision
Externí odkaz:
http://arxiv.org/abs/2202.01906
Autor:
Pfohl, Stephen R., Zhang, Haoran, Xu, Yizhe, Foryciarz, Agata, Ghassemi, Marzyeh, Shah, Nigam H.
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches
Externí odkaz:
http://arxiv.org/abs/2108.12250
Autor:
Schaekermann, Mike, Spitz, Terry, Pyles, Malcolm, Cole-Lewis, Heather, Wulczyn, Ellery, Pfohl, Stephen R., Martin, Donald, Jr., Jaroensri, Ronnachai, Keeling, Geoff, Liu, Yuan, Farquhar, Stephanie, Xue, Qinghan, Lester, Jenna, Hughes, Cían, Strachan, Patricia, Tan, Fraser, Bui, Peggy, Mermel, Craig H., Peng, Lily H., Matias, Yossi, Corrado, Greg S., Webster, Dale R., Virmani, Sunny, Semturs, Christopher, Liu, Yun, Horn, Ivor, Cameron Chen, Po-Hsuan
Publikováno v:
In eClinicalMedicine April 2024 70
Publikováno v:
Journal of Biomedical Informatics, Volume 113, January 2021, 103621
The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable attention and c
Externí odkaz:
http://arxiv.org/abs/2007.10306
Autor:
Steinberg, Ethan, Jung, Ken, Fries, Jason A., Corbin, Conor K., Pfohl, Stephen R., Shah, Nigam H.
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient re
Externí odkaz:
http://arxiv.org/abs/2001.05295
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive dat
Externí odkaz:
http://arxiv.org/abs/1911.05861