Dr. Suchi Saria

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Adarsh SubbaswamyU.S. Food and Drug Administration確認したメールアドレス: fda.hhs.gov
Peter SchulamAmazon確認したメールアドレス: amazon.com
Katharine HenryData Scientist at Janssen R&D | PhD Computer Science from Johns Hopkins University確認したメールアドレス: jhu.edu
Andong Zhan (展安东)Snowflake Inc.確認したメールアドレス: snowflake.com
Roy AdamsJohns Hopkins University確認したメールアドレス: jhu.edu
Daphne KollerCEO and Founder, insitro確認したメールアドレス: insitro.com
David N. HagerAssociate Professor of Medicine, Johns Hopkins University確認したメールアドレス: jhmi.edu
David BatesProfessor of Medicine, Harvard Medical School確認したメールアドレス: partners.org
Hossein SoleimaniUniversity of California, San Francisco確認したメールアドレス: ucsf.edu
E. Ray DorseyProfessor of Neurology, University of Rochester確認したメールアドレス: chet.rochester.edu
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of Toronto確認したメールアドレス: utoronto.ca
Isaac KohaneHarvard Medical School, Children's Hospital, Brigham and Women's Hospital確認したメールアドレス: hms.harvard.edu
Max A LittleUniversity of Birmingham確認したメールアドレス: mit.edu
Peter Pronovost, MD, PhDChief Clinical Transformation Officer University Hosptials Cleveland確認したメールアドレス: uhhospitals.org
Lucila Ohno-MachadoUniversity of California San Diego確認したメールアドレス: ucsd.edu
Jenna WiensUniversity of Michigan確認したメールアドレス: umich.edu
Atul J. ButteDirector, Bakar Computational Health Sciences Institute, University of California, San Francisco確認したメールアドレス: ucsf.edu
Finale Doshi-VelezProfessor, Harvard確認したメールアドレス: seas.harvard.edu
Dr. Marzyeh GhassemiAssociate Professor, EECS/IMES, MIT確認したメールアドレス: mit.edu
Andreas TerzisGoogle Deepmind確認したメールアドレス: google.com

フォロー

Dr. Suchi Saria

John C. Malone Endowed Chair; Computer Science, Statistics, Medicine, Health Policy, Johns Hopkins

確認したメールアドレス: cs.jhu.edu - ホームページ

AI Health AI Machine Learning Reasoning under Uncertainty Causal Inference


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Big data in health care: using analytics to identify and manage high-risk and high-cost patients DW Bates, S Saria, L Ohno-Machado, A Shah, G Escobar Health Affairs 33 (7), 1123-1131, 2014	1494	2014
Do no harm: a roadmap for responsible machine learning for health care (vol 25, pg 1337, 2019) J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, ... NATURE MEDICINE 25 (10), 1627-1627, 2019	845*	2019
Do no harm: a roadmap for responsible machine learning for health care J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ... Nature Medicine, 1-4, 2019	845	2019
A targeted real-time early warning score (TREWScore) for septic shock KE Henry, DN Hager, PJ Pronovost, S Saria Science Translational Medicine 7 (299), 299ra122-299ra122, 2015	644	2015
The Clinician and Dataset Shift in Artificial Intelligence SG Finlayson, A Subbaswamy, K Singh, J Bowers, A Kupke, J Zittrain, ... New England Journal of Medicine 385 (3), 283-286, 2021	521	2021
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist B Norgeot, G Quer, BK Beaulieu-Jones, A Torkamani, R Dias, ... Nature Medicine 26 (9), 1320-1324, 2020	432	2020
Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: the Mobile Parkinson Disease Score A Zhan, S Mohan, C Tarolli*, RB Schneider, JL Adams, S Sharma, ... JAMA Neurology, 2018	417	2018
Artificial intelligence in health care: The hope, the hype, the promise, the peril M Matheny, ST Israni, M Ahmed, D Whicher Washington, DC: National Academy of Medicine 10, 2019	411	2019
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ... bmj 377, 2022	408	2022
Integration of early physiological responses predicts later illness severity in preterm infants S Saria, AK Rajani, J Gould, D Koller, AA Penn Science translational medicine 2 (48), 48ra65-48ra65, 2010	298*	2010
Microsoft Cambridge at TREC 13: Web and Hard Tracks. H Zaragoza, N Craswell, MJ Taylor, S Saria, SE Robertson TREC 4, 1-1, 2004	260	2004
From development to deployment: dataset shift, causality, and shift-stable models in health AI A Subbaswamy, S Saria Biostatistics 21 (2), 345-352, 2020	257	2020
Reliable Decision Support Using Counterfactual Models P Schulam, S Saria Neural Information Processing Systems (NIPS), 2017	241	2017
Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis R Adams, KE Henry, A Sridharan, H Soleimani, A Zhan, N Rawat, ... Nature medicine 28 (7), 1455-1460, 2022	187	2022
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport A Subbaswamy, P Schulam, S Saria Artificial Intelligence and Statistics (AISTATS), 2018	184	2018
Subtyping: What it is and its role in precision medicine S Saria, A Goldenberg IEEE Intelligent Systems 30 (4), 70-75, 2015	147	2015
Developing Predictive Models Using Electronic Medical Records: Challenges and Pitfalls C Paxton, A Niculescu-Mizil, S Saria AMIA Annual Symposium Proceedings 2013, 1109, 2013	143	2013
Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system KE Henry, R Kornfield, A Sridharan, RC Linton, C Groh, T Wang, A Wu, ... npj Digital Medicine 5 (1), 1-6, 2022	129	2022
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery P Schulam, F Wigley, S Saria American Association for Artificial Intelligence 29 (3), 2956-2964, 2015	128	2015
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning P Schulam, S Saria Artificial Intelligence and Statistics (AISTATS), 2019	126	2019

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