Farah Shamout

According to our database, Farah Shamout authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review.

Saeed Shurrab, Alejandro Guerra-Manzanares, Amani Magid, Bartlomiej Piechowski-Jozwiak, Seyed Farokh Atashzar, Farah E. Shamout

IEEE J. Biomed. Health Informatics, November, 2024

The Role of Functional Muscle Networks in Improving Hand Gesture Perception for Human-Machine Interfaces.

Costanza Armanini, Tuka Alhanai, Farah E. Shamout, Seyed Farokh Atashzar

CoRR, 2024

Multi-modal Masked Siamese Network Improves Chest X-Ray Representation Learning.

Saeed Shurrab, Alejandro Guerra-Manzanares, Farah E. Shamout

CoRR, 2024

2023

Informative Priors Improve the Reliability of Multimodal Clinical Data Classification.

L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout

CoRR, 2023

Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data.

Yiqiu Shen, Jungkyu Park, Frank Yeung, Eliana Goldberg, Laura Heacock, Farah Shamout, Krzysztof J. Geras

CoRR, 2023

Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives.

Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos, Farah E. Shamout

Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

2022

Data Pre-Processing Using Neural Processes for Modeling Personalized Vital-Sign Time-Series Data.

Pulkit Sharma, Farah E. Shamout, Vinayak Abrol, David A. Clifton

IEEE J. Biomed. Health Informatics, 2022

Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients.

Sarmad Mehrdad, Farah E. Shamout, Yao Wang, Seyed Farokh Atashzar

CoRR, 2022

MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images.

Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout

Proceedings of the Machine Learning for Healthcare Conference, 2022

An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates.

David Johnson, Mohammad Alsharid, Rasheed El-Bouri, Nigel Mehdi, Farah Shamout, Alexandre Szenicer, David Toman, Saqr Binghalib

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.

Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanislaw Jastrzebski, Jan Witowski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

npj Digit. Medicine, 2021

Towards dynamic multi-modal phenotyping using chest radiographs and physiological data.

Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout

CoRR, 2021

Meta-repository of screening mammography classifiers.

Benjamin Stadnick, Jan Witowski, Vishwaesh Rajiv, Jakub Chledowski, Farah E. Shamout, Kyunghyun Cho, Krzysztof J. Geras

CoRR, 2021

Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs.

Nasir Hayat, Hazem Lashen, Farah E. Shamout

CoRR, 2021

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction.

Anuroop Sriram, Matthew J. Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore

CoRR, 2021

Explainability Matters: Backdoor Attacks on Medical Imaging.

Munachiso Nwadike, Takumi Miyawaki, Esha Sarkar, Michail Maniatakos, Farah Shamout

CoRR, 2021

The strategic pursuit of artificial intelligence in the United Arab Emirates.

Farah E. Shamout, Dana Abu Ali

Commun. ACM, 2021

Multi-Label Generalized Zero Shot Learning for the Classiffcation of Disease in Chest Radiographs.

Nasir Hayat, Hazem Lashen, Farah E. Shamout

Proceedings of the Machine Learning for Healthcare Conference, 2021

2020

Deep Interpretable Early Warning System for the Detection of Clinical Deterioration.

Farah E. Shamout, Tingting Zhu, Pulkit Sharma, Peter J. Watkinson, David A. Clifton

IEEE J. Biomed. Health Informatics, 2020

Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study.

Ghadeer O. Ghosheh, Bana Alamad, Kai-Wen Yang, Faisil Syed, Nasir Hayat, Imran Iqbal, Fatima Al Kindi, Sara Al Junaibi, Maha Al Safi, Raghib Ali, Walid Zaher, Mariam Al Harbi, Farah E. Shamout

CoRR, 2020

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.

Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanislaw Jastrzebski, Duo Wang, Ben Zhang, Siddhant Dogra, Meng Cao, Narges Razavian, David Kudlowitz, Lea Azour, William Moore, Yvonne W. Lui, Yindalon Aphinyanaphongs, Carlos Fernandez-Granda, Krzysztof J. Geras

CoRR, 2020

2019

Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality.

Pulkit Sharma, Farah E. Shamout, David A. Clifton

CoRR, 2019

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis.

Yang Yang, Timothy M. Walker, A. Sarah Walker, Daniel J. Wilson, Timothy E. A Peto, Derrick W. Crook, Farah Shamout, CRyPTIC Consortium, Tingting Zhu, David A. Clifton

Bioinform., 2019

2018

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.

Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian V. Dalca, Samuel G. Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

CoRR, 2018