Dr. Marzyeh Ghassemi
Dr. Marzyeh Ghassemi
Associate Professor, EECS/IMES, MIT
Vahvistettu sähköpostiosoite verkkotunnuksessa - Kotisivu
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
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
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
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
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
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
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
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
Challenges to the reproducibility of machine learning models in health care
AL Beam, AK Manrai, M Ghassemi
Jama 323 (4), 305-306, 2020
Unfolding Physiological State: Mortality Modelling in Intensive Care Units
M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ...
KDD 2014, 2014
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
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
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
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
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
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
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
Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach
A Raghu, M Komorowski, LA Celi, P Szolovits, M Ghassemi
Machine Learning for Healthcare Conference, 147-163, 2017
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
Deep Reinforcement Learning for Sepsis Treatment
A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi
arXiv preprint arXiv:1711.09602, 2017
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Artikkelit 1–20