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Federated learning for predicting clinical outcomes in patients with COVID-19.
Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antônio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Díez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Dayan I, et al. Among authors: rockenbach mabc. Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3. Epub 2021 Sep 15. Nat Med. 2021. PMID: 34526699 Free PMC article.
Federated Learning used for predicting outcomes in SARS-COV-2 patients.
Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, César de Antônio Corradi G, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Bezerra Cavalcanti Rockenbach MA, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Cruz E Silva PM, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Keshava Murthy KN, Fu LC, Furtado de Mendonça MR, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lima Lavor V, Rakvongthai Y, Lee YR, Wen Y. Flores M, et al. Res Sq [Preprint]. 2021 Jan 8:rs.3.rs-126892. doi: 10.21203/rs.3.rs-126892/v1. Res Sq. 2021. PMID: 33442676 Free PMC article. Updated. Preprint.
Clinical evaluation of k-space correlation informed motion artifact detection in segmented multi-slice MRI.
Jang I, Hoffmann M, Singh N, Balbastre Y, Chen L, Rockenbach MABC, Dalca A, Aganj I, Kalpathy-Cramer J, Fischl B, Frost R. Jang I, et al. Among authors: rockenbach mabc. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib. 2023 Jun;2023:3425. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib. 2023. PMID: 37565069 Free PMC article.
Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.
Ebrahimian S, Homayounieh F, Rockenbach MABC, Putha P, Raj T, Dayan I, Bizzo BC, Buch V, Wu D, Kim K, Li Q, Digumarthy SR, Kalra MK. Ebrahimian S, et al. Among authors: rockenbach mabc. Sci Rep. 2021 Jan 13;11(1):858. doi: 10.1038/s41598-020-79470-0. Sci Rep. 2021. PMID: 33441578 Free PMC article.
Validation of a Smartphone Application in the Evaluation and Treatment of Acute Stroke in a Comprehensive Stroke Center.
Martins SCO, Weiss G, Almeida AG, Brondani R, Carbonera LA, de Souza AC, Martins MCO, Nasi G, Nasi LA, Batista C, Sousa FB, Rockenbach MABC, Gonçalves FM, Vedolin LM, Nogueira RG. Martins SCO, et al. Among authors: rockenbach mabc. Stroke. 2020 Jan;51(1):240-246. doi: 10.1161/STROKEAHA.119.026727. Epub 2019 Dec 18. Stroke. 2020. PMID: 31847753 Clinical Trial.