Farah Shamout
Farah Shamout
NYU Clinical AI Lab
Zweryfikowany adres z nyu.edu
Cytowane przez
Cytowane przez
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
Y Shen, FE Shamout, JR Oliver, J Witowski, K Kannan, J Park, N Wu, ...
Nature communications 12 (1), 5645, 2021
Machine Learning for Clinical Outcome Prediction
FE Shamout, T Zhu, DA Clifton
IEEE Reviews in Biomedical Engineering, 2020
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ...
NPJ digital medicine 4 (1), 80, 2021
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration
FE Shamout, T Zhu, P Sharma, PJ Watkinson, DA Clifton
IEEE Journal of Biomedical and Health Informatics 24 (2), 437-446, 2020
Covid-19 prognosis via self-supervised representation learning and multi-image prediction
A Sriram, M Muckley, K Sinha, F Shamout, J Pineau, KJ Geras, L Azour, ...
arXiv preprint arXiv:2101.04909, 2021
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
Y Yang, TM Walker, AS Walker, DJ Wilson, TEA Peto, DW Crook, ...
Bioinformatics 35 (18), 3240-3249, 2019
Enhancement of non-invasive trans-membrane drug delivery using ultrasound and microbubbles during physiologically relevant flow
FE Shamout, AN Pouliopoulos, P Lee, S Bonaccorsi, L Towhidi, R Krams, ...
Ultrasound in medicine & biology 41 (9), 2435-2448, 2015
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
P Sharma, FE Shamout, DA Clifton
NeurIPS 2019 Workshop AI for Social Good arXiv preprint arXiv:1912.00354, 2019
Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign …
F Shamout, T Zhu, L Clifton, J Briggs, D Prytherch, P Meredith, ...
BMJ open 9 (11), e033301, 2019
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
N Hayat, KJ Geras, FE Shamout
Machine Learning for Healthcare Conference, 479-503, 2022
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
N Hayat, H Lashen, FE Shamout
Machine Learning for Healthcare Conference, 461-477, 2021
Data pre-processing using neural processes for modeling personalized vital-sign time-series data
P Sharma, FE Shamout, V Abrol, DA Clifton
IEEE Journal of Biomedical and Health Informatics 26 (4), 1528-1537, 2021
Meta-repository of screening mammography classifiers
B Stadnick, J Witowski, V Rajiv, J Chłędowski, FE Shamout, K Cho, ...
arXiv preprint arXiv:2108.04800, 2021
Development and validation of early warning score systems for COVID-19 patients
A Youssef, S Kouchaki, F Shamout, J Armstrong, R El-Bouri, T Taylor, ...
Healthcare Technology Letters, 1-12, 2021
Explainability Matters: Backdoor Attacks on Medical Imaging
M Nwadike, T Miyawaki, E Sarkar, M Maniatakos, F Shamout
AAAI 2021 Workshop on Trustworthy AI for Healthcare, 2020
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
A Guerra-Manzanares, LJL Lopez, M Maniatakos, FE Shamout
International Workshop on Trustworthy Machine Learning for Healthcare, 25-40, 2023
The strategic pursuit of artificial intelligence in the United Arab Emirates
FE Shamout, DA Ali
Communications of the ACM 64 (4), 57-58, 2021
An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates
D Johnson, M Alsharid, R El-Bouri, N Mehdi, F Shamout, A Szenicer, ...
AAAI Symposium on Educational Advances in Artificial Intelligence, 2022
Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs
S Mehrdad, FE Shamout, Y Wang, SF Atashzar
Scientific reports 13 (1), 9968, 2023
Machine learning for health (ML4H) workshop at NeurIPS 2018
N Antropova, AL Beam, BK Beaulieu-Jones, I Chen, C Chivers, A Dalca, ...
arXiv preprint arXiv:1811.07216, 2018
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