Dr. Marzyeh Ghassemi

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Leo Anthony CeliMassachusetts Institute of TechnologyVerifisert e-postadresse på mit.edu
Peter SzolovitsProfessor of Computer Science and Engineering, MITVerifisert e-postadresse på mit.edu
Haoran ZhangPhD Student, Massachusetts Institute of TechnologyVerifisert e-postadresse på mit.edu
Matthew B. A. McDermottPostdoctoral Researcher, Harvard Medical School, Department of Biomedical InformaticsVerifisert e-postadresse på hms.harvard.edu
Tristan NaumannPrincipal Researcher, Microsoft Research Health FuturesVerifisert e-postadresse på microsoft.com
Irene Y. ChenAssistant Professor, UC Berkeley and UC San FranciscoVerifisert e-postadresse på berkeley.edu
Anna GoldenbergScientist, SickKids Research Institute; Assistant Professor Department of Computer Science, University of TorontoVerifisert e-postadresse på utoronto.ca
Karsten RothELLIS, IMPRS-IS, University of TübingenVerifisert e-postadresse på uni-tuebingen.de
Lauren Oakden-RaynerAustralian Institute for Machine Learning. University of Adelaide. Royal Adelaide Hospital.Verifisert e-postadresse på adelaide.edu.au
Joseph Paul CohenAmazon, AIMI (Stanford University), Mila (Quebec AI Institute)Verifisert e-postadresse på stanford.edu
Harini SureshPhD Student, Massachusetts Institute of Technology (MIT)Verifisert e-postadresse på mit.edu
John GuttagVerifisert e-postadresse på mit.edu
Shalmali JoshiColumbia UniversityVerifisert e-postadresse på cumc.columbia.edu
Andrew L. BeamCTO FL97, Inc. | Associate Professor, Harvard UniversityVerifisert e-postadresse på hms.harvard.edu
Rajesh RanganathAssistant Professor, NYUVerifisert e-postadresse på cs.princeton.edu
Judy Wawira GichoyaEmory UniversityVerifisert e-postadresse på emory.edu
Bret NestorPostdoctoral Researcher, University of WashingtonVerifisert e-postadresse på cs.washington.edu
Daryush D. Mehta, PhDDirector of Voice Science and Technology Laboratory, Center for Laryngeal Surgery and VoiceVerifisert e-postadresse på alum.mit.edu
Jarrad H Van StanHarvard Medical School, Massachusetts General Hospital, MGH Institute of Health ProfessionsVerifisert e-postadresse på mghihp.edu
Natalie DullerudPh.D. Student, Stanford UniversityVerifisert e-postadresse på stanford.edu

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Dr. Marzyeh Ghassemi

Associate Professor, EECS/IMES, MIT

Verifisert e-postadresse på mit.edu - Startside

Healthy ML Robustness/Equity Machine Learning


Tittel Sorter etter sitater Sorter etter år Sorter etter tittel	Sitert av Sitert av	År
COVID-19 Image Data Collection: Prospective Predictions Are the Future JP Cohen, P Morrison, L Dao, K Roth, TQ Duong, M Ghassemi arXiv preprint arXiv:2006.11988, 2020	972	2020
The false hope of current approaches to explainable artificial intelligence in health care M Ghassemi, L Oakden-Rayner, AL Beam The Lancet Digital Health 3 (11), e745-e750, 2021	886	2021
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 25 (9), 1337-1340, 2019	840	2019
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations L Seyyed-Kalantari, H Zhang, M McDermott, IY Chen, M Ghassemi Nature medicine 27 (12), 2176-2182, 2021	557	2021
Ethical machine learning in healthcare IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi Annual Review of Biomedical Data Science 4, 123-144, 2021	518	2021
AI recognition of patient race in medical imaging: a modelling study JW Gichoya, I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, ... The Lancet Digital Health 4 (6), e406-e414, 2022	497*	2022
A Review of Challenges and Opportunities in Machine Learning for Health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 191, 2020	475*	2020
Can AI Help Reduce Disparities in General Medical and Mental Health Care? IY Chen, P Szolovits, M Ghassemi AMA Journal of Ethics 21 (2), 167-179, 2019	389	2019
Do as AI say: susceptibility in deployment of clinical decision-aids S Gaube, H Suresh, M Raue, A Merritt, SJ Berkowitz, E Lermer, ... NPJ digital medicine 4 (1), 31, 2021	334	2021
CheXclusion: Fairness gaps in deep chest X-ray classifiers L Seyyed-Kalantari, G Liu, M McDermott, IY Chen, M Ghassemi BIOCOMPUTING 2021: Proceedings of the Pacific Symposium, 232-243, 2020	333	2020
Challenges to the reproducibility of machine learning models in health care AL Beam, AK Manrai, M Ghassemi Jama 323 (4), 305-306, 2020	329	2020
Clinically accurate chest x-ray report generation G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, ... Machine Learning for Healthcare Conference, 249-269, 2019	318	2019
TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods GS Collins, KGM Moons, P Dhiman, RD Riley, AL Beam, B Van Calster, ... bmj 385, 2024	308	2024
Unfolding Physiological State: Mortality Modelling in Intensive Care Units M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ... KDD 2014, 2014	294	2014
Clinical Intervention Prediction and Understanding with Deep Neural Networks H Suresh, N Hunt, A Johnson, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 322-337, 2017	288*	2017
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in icu with sparse, heterogeneous clinical data M Ghassemi, MAF Pimentel, T Naumann, T Brennan, DA Clifton, ... Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015	286	2015
Predicting COVID-19 pneumonia severity on chest X-ray with deep learning JP Cohen, L Dao, K Roth, P Morrison, Y Bengio, AF Abbasi, B Shen, ... Cureus 12 (7), 2020	281	2020
Reproducibility in machine learning for health research: Still a ways to go MBA McDermott, S Wang, N Marinsek, R Ranganath, L Foschini, ... Science Translational Medicine 13 (586), eabb1655, 2021	278*	2021
The role of machine learning in clinical research: transforming the future of evidence generation EH Weissler, T Naumann, T Andersson, R Ranganath, O Elemento, Y Luo, ... Trials 22, 1-15, 2021	262	2021
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 222-235, 2020	262	2020

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