Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Farah E. Shamout"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. Although such approaches deliver promising results, they do not take advantage of the associated patient or scan information colle
Externí odkaz:
https://doaj.org/article/f8f7b9d27cc94b60812525bda035cbf7
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical
Externí odkaz:
https://doaj.org/article/c834a5e74155438facf775a9e35a42ba
Autor:
Ghadeer O. Ghosheh, Terrence Lee St John, Pengyu Wang, Vee Nis Ling, Lelan R. Orquiola, Nasir Hayat, Farah E. Shamout, Y. Zaki Almallah
Publikováno v:
PLOS Digital Health, Vol 2, Iss 11 (2023)
Externí odkaz:
https://doaj.org/article/de974366c80f47ddaec15129f2de818d
Autor:
Yiqiu Shen, Farah E. Shamout, Jamie R. Oliver, Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, Connor Huddleston, Stacey Wolfson, Alexandra Millet, Robin Ehrenpreis, Divya Awal, Cathy Tyma, Naziya Samreen, Yiming Gao, Chloe Chhor, Stacey Gandhi, Cindy Lee, Sheila Kumari-Subaiya, Cindy Leonard, Reyhan Mohammed, Christopher Moczulski, Jaime Altabet, James Babb, Alana Lewin, Beatriu Reig, Linda Moy, Laura Heacock, Krzysztof J. Geras
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level
Externí odkaz:
https://doaj.org/article/c9426dec81ff479488e813049d5bf81a
Autor:
Farah E. Shamout, Yiqiu Shen, Nan Wu, Aakash Kaku, Jungkyu Park, Taro Makino, Stanisław Jastrzębski, 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
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
Abstract During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration ris
Externí odkaz:
https://doaj.org/article/39490bddaa344a02b06a3d77ab46dd52
Autor:
Farah E. Shamout
Publikováno v:
Health Informatics ISBN: 9783031176654
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0d1d9d25650790b30873166415e2296
https://doi.org/10.1007/978-3-031-17666-1_7
https://doi.org/10.1007/978-3-031-17666-1_7
Autor:
Farah E Shamout, Phillip Wang, Nasir Hayat, Vee Nis Ling, Terrence Lee St John, Ghadeer Ghosheh, Lelan Orquiola, Vansh Gadhia, Zaki Almallah
Publikováno v:
Part I: ePapers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d369f675202162cb7a9e715075d96c0
https://doi.org/10.1136/bmjhci-2022-fciasc.10
https://doi.org/10.1136/bmjhci-2022-fciasc.10
Autor:
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
Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To sup
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c07704921c80753a48816b15934f775f
Publikováno v:
Healthcare Informatics Research
Healthcare Informatics Research, Vol 27, Iss 2, Pp 168-171 (2021)
Healthcare Informatics Research, Vol 27, Iss 2, Pp 168-171 (2021)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::986f8383ebeac5896cdc4d7ca78879ce
https://doi.org/10.4258/hir.2021.27.2.168
https://doi.org/10.4258/hir.2021.27.2.168
Publikováno v:
IEEE Reviews in Biomedical Engineering. 14:116-126
Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome predicti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc8dfd49e90219d1e7eff18c73407362
https://doi.org/10.1109/rbme.2020.3007816
https://doi.org/10.1109/rbme.2020.3007816