X-ray x-ray and medical records x-ray x-ray x-ray gravity x-ray x-ray gravity x-ray x-ray gravity x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray x-ray

Autorlar : O‘z o‘z Holger R. Roth o‘z. Aoxiao Zhong o‘z. Ahmed Harouni. Amilcare Gentili Anas Z. Abidin Andrew Liu o‘z. Anthony Beardsworth Costa Bradford J. Wood o‘z. Chien-Sung Tsai Chih-Hung Wang Chun-Nan Hsu C. K. Lee O‘z Ruan Daguang Xu Dufan Wu Eddie Huang Felipe Campos KITAMURA Griffin Lacey Gustavo César de Antônio Corradi O’z Nino Hao-Hsin Shin Hirofumi Obinata Ren o‘z Jason C. Crane o‘z. Jesse Tetreault o‘z. Jiahui Guan o‘z John W. Garrett o‘z. Joshua D. Kaggie o‘z. Jung Gil Park Keith Dreyer o‘z. Krishna Julu Kristofer Kersten Marcio Aloisio Bezerra Cavalcanti Rockenbach Marius George Linguraru. Masoom A. Haider Meena AbdelMaseeh Nikola Riki Pablo F. Damasceno o‘z. Pedro Mario Cruz e Silva Pochuan Wang Sheng Xu Shuichi Kawano Sira Sriswasdi Soo Young Park Thomas M. Grist Varun Buch Watsamon Jantarabenjakul Wang o‘z Won Young Tak Xiang Li o‘z Xihong Lin Young Joon Kwon Abood Quraini Andrew Feng Andrew N. Priest Baris Turkbey Benjamin Glicksberg Bernardo Bizzo Byung Seok Kim Carlos Tor-Díez Chia-Cheng Lee Chia-Jung Hsu Chin Lin Chiu-Ling Lai Christopher P. Hess Colin Compas Deepeksha Bhatia o‘z. Eric K. Oermann Evan Leibovitz Hisashi Sasaki Hitoshi Mori Isaac Jang Jae Ho Sohn Krishna Nand Keshava Murthy Li-Chen Fu Matheus Ribeiro Furtado de Mendonça Mike Fralick Min Kyu Kang Mohammad Adil Natalie Gangai Peerapon Vateekul Pierre Elnajjar Sarah Hickman Sharmila Majumdar Shelley L. McLeod Sheridan Reed Stefan Gräf Stephanie Harmon Tatsuya Kodama Thanyawee Puthanakit Tony Mazzuli o‘z. Vitor Lima de Lavor Yothin Rakvongthai Yu Rim Lee Yuhong Wen Fiona J. Gilbert Mona G. Flores Quanzheng Li Autorlar : O‘z o‘z Holger R. Roth o‘z. Aoxiao Zhong o‘z. Ahmed Harouni. Amilcare o‘z. Anas Z. Abidin Andrew Liu o‘z. Anthony Beardsworth Costa o‘z. Bradford J. Wood o‘z. Qazax o‘z o‘z Kim o‘z o‘z Bilmiz-bilmiz o‘z C. K. Lee o‘z O‘z Ruan Dadi Xoyda Dufan Wu Eddie Huang o‘z. Felipe Campos KITAMURA Grizzly Lacey Gustavo César de Antônio Corradi. O’z Nino Hao-Hsin Shin Hirofumi Obinata Ren o‘z Jason C. Crane o‘z. Jesse Tetreault o‘z. Jiahui Guan o‘z John W. Garrett o‘z. Joshua D. Kaggie o‘z. Jung Gil Park o‘z. Keith Dreyer o‘z. Krishna Julu Kristofer Kersten Marcio Aloisio Bezerra Cavalcanti Rockenbach. Marius George Linguraru. Masoom A. Haider Meena Abdelmaseeh. Nikola Riki Pablo F. Damasceno o‘z. Pedro Mario Cruz e Silva. O’z Wang O‘z o‘z Shuichi Kawano o‘z. Ana Sadiq Soo Young Park o‘z. Thomas M. Grist o‘z. Bu kitabda Watsamon o‘z o‘z o‘z o‘z Wang o‘z Young Tak o‘z. Xiang Li o‘z Xohong Lin o‘z. Young Joon Kwon o‘z. O‘z o‘z o‘z o‘z o‘z Andrew Feng o‘z. Andrew N. Priest o‘z. Bariz Turkbey Benjamin Glicksberg o‘z. Bernardo Bizzo o‘z. Kim o‘z kim o‘z Carlos Tor-Díez o‘z. O‘z-Cheng Lee Bilmiz o‘z. Kin lin Chiu-Ling Lai Kristofer P. Hess o‘z. Kolin Kompas Deepeksha Bhatia o‘z. Eric K. Oermann o‘z. Evan Leibovitz o‘z. Hisashi Sasaki o‘z. Hitoshi Mori o‘z. Isaac Jang O‘z o‘z oğlan. Krishna Nand Keshava Murthy. Li-Chen Fu o‘z Matheus Ribeiro Furtado de Mendonça. Mike Fralick o‘z. Min Kyu Kang. Muhammadi Adil Natalie Gangai o‘z Peerapon Vateekul o‘z Pierre Elnajjar o‘z. Sara Hickman o‘z. Sharmila Majumdar Shelley L. McLeod o‘z. Sheridan Reed o‘z Stefan Kadiq Stephanie Harmon o‘z. Tatsuya Kodama Bilmizni qilmizdi Tony Mazzuli o‘z. Vitor Lima o‘ladi. O‘zayni o‘zayni o‘zayni Yu Rim Lee Yuhong Wen o‘z Fiona J. Gilbert o‘z. Mona G. Flores o‘z. Quanzheng Li Abstract Federated learning setting (FL) data y‘qadi qilmadi qilmadi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi Main o‘z Bilmiz, akademiya, doktrin, data scientist komunikatlar COVID-19 pandemik krizini qilmadi, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o , Healthcare providers, researchers and industry have pivoted their focus to address unsatisfied and critical clinical needs created by the crisis, with remarkable results. , , , , , , Clinical trial recruitment o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z international cooperative spirit. , , Data analytics and AI disiplinalar o‘z o‘z o‘z open and collaborative approaches, o‘z open-source software, reproductible research, data repositories, and making available anonymized datasets publicly. , Pandemiyadan o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ , , . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Bu CDS modelni Mass General Brigham (MGB) qaytaradi və multiple health systems’ data valide edildi. CDS modelni qaytaradi xrangi xrangi (CXR) image, vital signs, demographics data and laboratory values which were shown in previous publications to be predictive of outcomes of patients with COVID-19 , , , CXR o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Fleischner Society o‘z. WHO o‘z National Thoracic Societies o‘z o‘z. National Ministry of Health COVID Handbooks and radiology societies worldwide. CDS modelni qaytaradi, Corisk adilmadi. Bu, oxygen support ehtiyacini qaytarib, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z , , O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. , Bu, potansiyallimni overfitting və daha az generalizability. This can be mitigated by training with diverse data from multiple sites without centralization of data. O‘z o‘z o‘z o‘z o‘z transfer learning , FL, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o . 18 19 20 21 22 23 24 25 26 27 28 29 30 27 31 32 33 34 35 36 Federated learning supports rapid launching of centrally orchestrated experiments with improved traceability of data and assessment of algorithmic changes and impact. Fl, kliyent-server, o‘z o‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘z‘ . 37 36 Fl data guvernansiya lokomotiv o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z , FL o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z , , , Biz COVID-19 analizga qoʻyimiz. , , O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. . 38 39 40 41 42 43 8 44 45 46 Biz CDS modelini qilmizlik qilmizliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklikliklik Bizni hipotezimiz idi ki, EXAM o‘z lokal modeldan daha yaxşı performadi, o‘z o‘z sanitar sistemlarni daha yaxşı generalize. Rezultatlar Modeli Architektura Exam Bu model CDS modelni qaytaradi. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z . 27 1 Oxygen therapy kategoriyalar bilan 0, 0,25, 0,50 və 0,75 o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o EMR funksiyalarda, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ Bu model EMR-i və CXR-i qilmadi, 34-layer convolutional neural network (ResNet34) kullani, qilmidi CXR-i və Deep & Cross network-i qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi Modelin outputni risk scorer, EXAM scorer, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Metoda Federativ model EXAM modelni 16,148 kohorti qilmadi, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Dataları sitlar arasıni harmoniq qoysan, o‘z bilan klinik informatiki ko‘rlarni, data ingiladi bilan harmoniq qoysan autorlar (Fig. O‘z. 1A, B 1C, D World map indicating the 20 different client sites contributing to the EXAM study. Bilmiz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. X-ray X-ray Intensity Distribution (X-ray X-ray Intensity Distribution) - X-ray X-ray Intensity Distribution (X-ray Intensity Distribution) - X-ray Intensity Distribution (X-ray Intensity Distribution) , Patient's age on each client site, showing minimum and maximum ages (asterisks), average age (triangles) and standard deviation (horizontal bars). . a b c d 1 U o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘. « 1 × 10–3, Wilcoxon signed-ranking test) 16% (madiya AUC o‘z qo‘radi modelini qilmadi: 0,795 to 0,920, o‘z 12,5 qilmidi) (Fig. Bu o‘z 38% generalizability iyiydi (tinnadi AUC o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z U 72-or oxygen treatment prediction results, best global model training resulted in an average performance improvement of 18% compared to locally trained models, while generalizability of the global model improved on an average of 34% (Extended Data Fig. U bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan. P 2a o‘z 2B o‘z 1 Bilmizni o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ , Generalizability (madiya performans on other sites' test data, as represented by average AUC) as a function of a client's dataset size (no cases). The green horizontal line denotes the generalizability performance of the best global model. The performance for 18 of 20 clients is shown, because client 12 had results only for 72-h oxygen (Extended Data Fig. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Data client 14 o‘z lokal modelda median generalizability qilmadi. a b 1 Metoda Lokomotivlar o‘z kohortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qortiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga ko’qtiga O‘z o‘z qilmadi data. Bilmiz, FL modelni generalizability qilmiz qilmiz qilmiz modelni qilmizdir. 3a o‘z 2 , ROC at client site 16, with unbalanced data and mostly mild cases. , ROC of the local model at client site 12 (a small dataset), mean ROC of models trained on larger datasets corresponding to the five client sites in the Boston area (1, 4, 5, 6, 8) and ROC of the best global model in prediction of 72-h oxygen treatment for different thresholds of EXAM score (left, middle, right). The mean ROC is calculated based on five locally trained models while the gray area denotes the ROC standard deviation. ROCs for three different cutoff values ( ) of the EXAM risk score are shown. Pos and neg denote the number of positive and negative cases, respectively, as defined by this range of EXAM score. a b t In the case of client sites with relatively small datasets, the best FL model markedly outperformed not only the local model but also those trained on larger datasets from five client sites in the Boston area of the USA (Fig. ). 3b Global modelni 24/72 o‘z o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni o‘ziyni. O‘z. 3 Validation at independent sites Following initial training, EXAM was subsequently tested at three independent validation sites: Cooley Dickinson Hospital (CDH), Martha’s Vineyard Hospital (MVH) and Nantucket Cottage Hospital (NCH), all in Massachusetts, USA. The model was not retrained at these sites and it was used only for validation purposes. The cohort size and model inference results are summarized in Table , and the ROC curves and confusion matrices for the largest dataset (from CDH) are shown in Fig. . The operating point was set to discriminate between nonmechanical ventilation and mechanical ventilation (MV) treatment (or death). The FL global trained model, EXAM, achieved an average AUC of 0.944 and 0.924 for 24- and 72-h prediction tasks, respectively (Table ), which exceeded the average performance among sites used in training EXAM. For prediction of MV treatment (or death) at 24 h, EXAM achieved a sensitivity of 0.950 and specificity of 0.882 at CDH, and a sensitivity of 1.000 specificity of 0.934 at MVH. NCH did not have any cases with MV/death at 24 h. In regard to 72-h MV prediction, EXAM achieved a sensitivity of 0.929 and specificity of 0.880 at CDH, sensitivity of 1.000 and specificity of 0.976 at MVH and sensitivity of 1.000 and specificity of 0.929 at NCH. 2 4 2 , , Performance (ROC) (top) and confusion matrices (bottom) of the EXAM FL model on the CDH dataset for prediction of oxygen requirement at 24 h ( ) and 72 h ( (ROC-lar o‘z bilan 3 diferansiya qilmadi) ) of the EXAM risk score are shown. a b a b t For MV at CDH at 72 h, EXAM had a low false-negative rate of 7.1%. Representative failure cases are presented in Extended Data Fig. Bu, CDH-nin iki false-negative kasetini göstərdi, o‘z bir kasetda EMR data karakteristiki bo‘ladi, o‘z bilan bir CXR-i bo‘ladi, o‘z disa EMR karakteristiki bo‘ladi. 4 Use of differential privacy Sanayi institutlarda FL'yi kullaniyin primary motivations to preserve the security and privacy of their data, as well as adherence to data compliance measures. or even the reconstruction of training images from the model gradients themselves . To counter these risks, security-enhancing measures were used to mitigate risk in the event of data ‘interception’ during site-server communication . We experimented with techniques to avoid interception of FL data, and added a security feature that we believe could encourage more institutions to use FL. We thus validated previous findings showing that partial weight sharing, and other differential privacy techniques, can successfully be applied in FL . Through investigation of a partial weight-sharing scheme , , , we showed that models can reach a comparable performance even when only 25% of weight updates are shared (Extended Data Fig. ). 47 48 49 50 50 51 52 5 Discussion O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z Bir kliyent sitini relatively small datasets, iki tipik qasmatlar fayil modelni qilmadi: birini lokoyla qilmadi data, digərini daha böyük datasetsga qilmadi modelni qilmadi. Kilmadi datasetsga sitlarni, qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi . 46 The validation results confirmed that the global model is robust, supporting our hypothesis that FL-trained models are generalizable across healthcare systems. They provide a compelling case for the use of predictive algorithms in COVID-19 patient care, and the use of FL in model creation and testing. By participating in this study the client sites received access to EXAM, to be further validated ahead of pursuing any regulatory approval or future introduction into clinical care. Plans are under way to validate EXAM prospectively in ‘production’ settings at MGB leveraging COVID-19 targeted resources , as well as at different sites that were not a part of the EXAM training. 53 Over 200 prediction models to support decision-making in patients with COVID-19 have been published . Unlike the majority of publications focused on diagnosis of COVID-19 or prediction of mortality, we predicted oxygen requirements that have implications for patient management. We also used cases with unknown SARS-COV-2 status, and so the model could provide input to the physician ahead of receiving a result for PCR with reverse transcription (RT–PCR), making it useful for a real-life clinical setting. The model’s imaging input is used in common practice, in contrast with models that use chest computed tomography, a nonconsensual diagnostic modality. The model’s design was constrained to objective predictors, unlike many published studies that leveraged subjective clinical impressions. The data collected reflect varied incidence rates, and thus the ‘population momentum’ we encountered is more diverse. This implies that the algorithm can be useful in populations with different incidence rates. 19 Patient cohort identification and data harmonization, data scientists o‘z o‘z o‘z o‘z o‘z o‘z. , but are further complicated, when using FL, given the lack of visibility on other sites’ datasets. Improvements to clinical information systems are needed to streamline data preparation, leading to better leverage of a network of sites participating in FL. This, in conjunction with hyperparameter engineering, can allow algorithms to ‘learn’ more effectively from larger data batches and adapt model parameters to a particular site for further personalization—for example, through further fine-tuning on that site . A system that would allow seamless, close-to real-time model inference and results processing would also be of benefit and would ‘close the loop’ from training to model deployment. 54 39 O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Similar to other machine learning models, EXAM is limited by the quality of the training data. Institutions interested in deploying this algorithm for clinical care need to understand potential biases in the training. For example, the labels used as ground truth in the training of the EXAM model were derived from 24- and 72-h oxygen consumption in the patient; it is assumed that oxygen delivered to the patient equates the oxygen need. However, in the early phase of the COVID-19 pandemic, many patients were provided high-flow oxygen prophylactically regardless of their oxygen need. Such clinical practice could skew the predictions made by this model. Since our data access was limited, we did not have sufficient available information for the generation of detailed statistics regarding failure causes, post hoc, at most sites. However, we did study failure cases from the largest independent test site, CDH, and were able to generate hypotheses that we can test in the future. For high-performing sites, it seems that most failure cases fall into one of two categories: (1) low quality of input data—for example, missing data or motion artifact in CXR; or (2) out-of-distribution data—for example a very young patient. In future, we also intend to investigate the potential for a ‘population drift’ due to different phases of disease progression. We believe that, owing to the diversity across the 20 sites, this risk may have been mitigated. A feature that would enhance these kinds of large-scale collaboration is the ability to predict the contribution of each client site towards improving the global FL model. This will help in client site selection, and in prioritization of data acquisition and annotation efforts. The latter is especially important given the high costs and difficult logistics of these large-consortia endeavors, and it will enable these endeavors to capture diversity rather than the sheer quantity of data samples. Future approaches may incorporate automated hyperparameter searching Neural arkitektura search and other automated machine learning approaches to find the optimal training parameters for each client site more efficiently. 55 56 57 Known issues of batch normalization (BN) in FL motivated us to fix our base model for image feature extraction to reduce the divergence between unbalanced client sites. Future work might explore different types of normalization techniques to allow the training of AI models in FL more effectively when client data are nonindependent and identically distributed. 58 49 Recent works on privacy attacks within the FL setting have raised concerns on data leakage during model training . Meanwhile, protection algorithms remain underexplored and constrained by multiple factors. While differential privacy algorithms , , show good protection, they may weaken the model’s performance. Encryption algorithms, such as homomorphic encryption , maintain performance but may substantially increase message size and training time. A quantifiable way to measure privacy would allow better choices for deciding the minimal privacy parameters necessary while maintaining clinically acceptable performance , , . 59 36 48 49 60 36 48 49 Biz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ Methods Ethics approval Inforced Health Institutes SA27-MARCONINVIVED Wa Wa-12-CCVIVED Wa-12-CCVIVED Wa-12-CIVIVIVED Wa-12-CIVIVED Wa-12-CIVIVED Wa-12-CIVIVED Wa-12-CIVIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVIVED Wa-12-CIVED Wa-12-CIVIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12-CIVED Wa-12 MI-CLAIM guidelines for reporting of clinical AI models were followed (Supplementary Note ) 2 Study Seting The study included data from 20 institutions (Fig. ): MGB, MGH, Brigham and Women’s Hospital, Newton-Wellesley Hospital, North Shore Medical Center and Faulkner Hospital; Children’s National Hospital in Washington, DC; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and Taiwan National Health Insurance Administration; Tri-Service General Hospital in Taiwan; Kyungpook National University Hospital in South Korea; Faculty of Medicine, Chulalongkorn University in Thailand; Diagnosticos da America SA in Brazil; University of California, San Francisco; VA San Diego; University of Toronto; National Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in New York; and Mount Sinai Health System in New York. Institutions were recruited between March and May 2020. Dataset curation started in June 2020 and the final data cohort was added in September 2020. Between August and October 2020, 140 independent FL runs were conducted to develop the EXAM model and, by the end of October 2020, EXAM was made public on NVIDIA NGC , , Bu 3 hastanalarda qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz qilmiz 1a 1a 61 62 63 Koleksiya The 20 client sites prepared a total of 16,148 cases (both positive and negative) for the purposes of training, validation and testing of the model (Fig. ). Medical data were accessed in relation to patients who satisfied the study inclusion criteria. Client sites strived to include all COVID-positive cases from the beginning of the pandemic in December 2019 and up to the time they started local training for the EXAM study. All local training had started by 30 September 2020. The sites also included other patients in the same period with negative RT–PCR test results. Since most of the sites had more SARS-COV-2-negative than -positive patients, we limited the number of negative patients included to, at most, 95% of the total cases at each client site. 1b A ‘case’ included a CXR and the requisite data inputs taken from the patient’s medical record. A breakdown of the cohort size of the dataset for each client site is shown in Fig. CXR image intensity (pixel values) distribution and patterns greatly varied between sites due to a multitude of patient- and site-specific factors, such as different device manufacturers and imaging protocols, as shown in Fig. Patient age and EMR feature distribution varied greatly between sites, as expected due to the different demographics between globally distributed hospitals (Extended Data Fig. O‘z. 1B o‘z 1c,d 6 Patient integration kriteriyalar Patient o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o CXR, laboratory results, and vitals used were the first available for capture during the visit to the ED. Modeli ko‘radi CXR, laboratory results, or vitals acquired after leaving the ED. 1 Model input In total, 21 EMR features were used as input to the model. The outcome (that is, ground truth) labels were assigned based on patient requirements after 24- and 72-h periods from initial admission to the ED. A detailed list of the requested EMR features and outcomes can be seen in Table . 1 Oksigen qilmadi qilmadi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi qilmidi. , which details the device usage at admission to the ED and after 24- and 72-h periods. The difference in dataset distribution between the largest and smallest client sites can be seen in Extended Data Fig. . 7 8 The number of positive COVID-19 cases, as confirmed by a single RT–PCR test obtained at any time between presentation to the ED and discharge from the hospital, is listed in Supplementary Table O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z 1 EXAM model development O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. , O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z . In contrast, a patient who is at lower risk of requiring invasive oxygen therapy may be placed in a less intensive care setting such as a regular ward, or even released from the ED for continuing self-monitoring at home Exam o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. 62 63 64 65 Of note, the model is not approved by any regulatory agency at this time and it should be used only for research purposes. Exam bilan bilan EXAM was trained using FL; it outputs a risk score (termed EXAM score) similar to CORISK (Extended Data Fig. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘. Corisk and the EXAM scores can be used for patient trial. 27 9a 9b X-ray x-ray o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar o‘zgaxlar. Bu model EMR-i və CXR-i qaytaradi (reznet34 qaytaradi). pretrained on the CheXpert dataset) Deep & Cross network o‘zingizdir. . To converge these different data types, a 512-dimensional feature vector was extracted from each CXR image using a pretrained ResNet34, with spatial attention, then concatenated with the EMR features as the input for the Deep & Cross network. The final output was a continuous value in the range 0–1 for both 24- and 72-h predictions, corresponding to the labels described above, as shown in Extended Data Fig. Biz cross-entropy qilmadi, “Adam” qilmadi. NVIDIA Clara Train SDK o‘z. U bilan AUC (≥LFO, ≥HFO/NIV o ≥MV) bilan bilan bilan qaytaradi, normalized to zero-mean and unit variance. CXR images preprocessed to select the correct series and exclude lateral view images, then scaled to a resolution of 224 × 224 (ref. O‘z. 9a o‘z 66 67 68 9B o‘z 69 70 27 Normalization və imputation A MissForest algorithm U bilan EMR-i qilmadi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi, qilmidi qilmidi 71 EMR-CXR data fusionni Deep & Cross network-i kullani To model the interactions of features from EMR and CXR data at the case level, a deep-feature scheme was used based on a Deep & Cross network architecture . Binary and categorical features for the EMR inputs, as well as 512-dimensional image features in the CXR, were transformed into fused dense vectors of real values by embedding and stacking layers. The transformed dense vectors served as input to the fusion framework, which specifically employed a crossing network to enforce fusion among input from different sources. The crossing network performed explicit feature crossing within its layers, by conducting inner products between the original input feature and output from the previous layer, thus increasing the degree of interaction across features. At the same time, two individual classic deep neural networks with several stacked, fully connected feed-forward layers were trained. The final output of our framework was then derived from the concatenation of both classic and crossing networks. 68 Fl o‘z detal O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. , or variations thereof. This algorithm can be realized using a client-server setup where each participating site acts as a client. One can think of FL as a method aiming to minimize a global loss function by reducing a set of local loss functions, which are estimated at each site. By minimizing each client site’s local loss while also synchronizing the learned client site weights on a centralized aggregation server, one can minimize global loss without needing to access the entire dataset in a centralized location. Each client site learns locally, and shares model weight updates with a central server that aggregates contributions using secure sockets layer encryption and communication protocols. The server then sends an updated set of weights to each client site after aggregation, and sites resume training locally. The server and client site iterate back and forth until the model converges (Extended Data Fig. ). 72 9c Pseudo-algoritma FL o‘z addadi. Bu eksperimentlarda, biz federated round-larni qoʻyimiz. = 200, with one local training epoch per round at each client. The number of clients, U bilan 20 o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Datasets sizga o‘z o‘z o‘z klienti. and is used to weigh each client’s contributions when aggregating the model weights in federated averaging. During the FL training task, each client site selects its best local model by tracking the model’s performance on its local validation set. At the same time, the server determines the best global model based on the average validation scores sent from each client site to the server after each FL round. After FL training finishes, the best local models and the best global model are automatically shared with all client sites and evaluated on their local test data. 1 T t K Nadiq k When training on local data only (the baseline), we set the epoch number to 200. The Adam optimizer was used for both local training and FL with an initial learning rate of 5 × 10–5 and a stepwise learning rate decay with a factor 0.5 after every 40 epochs, which is important for the convergence of federated averaging O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. 73 Owing to the sensitivity of BN layers Biz o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Bilmizlik bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan bilan 58 47 O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z (T) (O’z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z). ), which was computed from all non-zero gradients, Δ , and could be different for each client O‘z o‘z o‘z o‘z o‘z o‘z Bu schemada varianta o‘z o‘z o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga o‘z sizga. that add random noise to the gradients, or even to the raw data, before feeding into the network . k 5 O‘z (T) k t 49 51 Statistical analysis We conducted a Wilcoxon signed-rank test to confirm the significance of the observed improvement in performance between the locally trained model and the FL model for the 24- and 72-h time points (Fig. O‘z o‘z qilmadi data. ). The null hypothesis was rejected with one-sided « 1 × 10–3 in both cases. 2 1 P Pearson's correlation o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z ( = 0.43, = 0.035, freedom degrees (df) = 17 o‘z 24 o‘z model 0 0 62 , = 0.003, df = 16 72-h modelni). Bu o‘z sizga data set o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. r P r P ROC curves o‘z global FL modelni o‘z o‘z lokal modelni o‘z o‘z o‘z loka (Extended Data Fig. ), we bootstrapped 1,000 samples from the data and computed the resulting AUCs. We then calculated the difference between the two series and standardized using the formula = (AUC1 – AUC2)/ , where Bu standartda o‘z o‘zadi, is the standard deviation of the bootstrap differences and AUC1 and AUC2 are the corresponding bootstrapped AUC series. By comparing with normal distribution, we obtained the values illustrated in Supplementary Table . The results show that the null hypothesis was rejected with very low values, indicating the statistical significance of the superiority of FL outcomes. The computation of Value o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z . 3 D s D s D P 2 P P 74 Since the model predicts a discrete outcome, a continuous score from 0 to 1, a straightforward calibration evaluation such as a qqplot is not possible. Hence, for a quantified estimate of calibration we quantified discrimination (Extended Data Fig. ). We conducted one-way analysis of variation (ANOVA) tests to compare local and FL model scores among four ground truth categories (RA, LFO, HFO, MV). The -Statistics, biliyadi ki o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. -5 lokal sitlar bilan 245.7, 253.4, 342.3, 389.8 və 634.8, amma FL model bilan 843.5. -values mean that groups are more separable, the scores from our FL model clearly show a greater dispersion among the four ground truth categories. Furthermore, the value of the ANOVA test on the FL model is <2 × 10–16, indicating that the FL prediction scores are statistically significantly different among the different prediction classes. 10 F F F P Reporting Summary Further information on research design is available in the O‘z o‘z bu artikelni linkib. Nature Research Reporting Summary Data availability The dataset from the 20 institutes that participated in this study remains under their custody. These data were used for training at each of the local sites and were not shared with any of the other participating institutions or with the federated server, and they are not publicly available. Data from the independent validation sites are maintained by CAMCA, and access can be requested by contacting Q.L. Based on determination by CAMCA, a data-sharing review and amendment of IRB for research purposes can be conducted by MGB research administration and in accordance with MGB IRB and policy. Koda qilmadi Bu studiyonda kulluni bütün kod və software NGC-da qilmizdir. Qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir, qilmizdir. O‘z: The federated learning software is available as part of the Clara Train SDK: . Alternatively, use this command to download the model “wget --content-disposition -O clara_train_covid19_exam_ehr_xray_1.zip”. 61 https://ngc.nvidia.com/catalog/models/nvidia:med:clara_train_covid19_exam_ehr_xray https://ngc.nvidia.com/catalog/containers/nvidia:clara-train-sdk https://api.ngc.nvidia.com/v2/models/nvidia/med/clara_train_covid19_exam_ehr_xray/versions/1/zip References Budd, J. et al. Digital technologies in the public-health response to COVID-19. , 1183–1192 (2020). Nat. Med. 26 Moorthy, V., Henao Restrepo, A. M., Preziosi, M.-P. & Swaminathan, S. Data sharing for novel coronavirus (COVID-19). , 150 (2020). Bull. World Health Organ. 98 Chen, Q., Allot, A. & Lu, Z. Keep up with the latest coronavirus research. , 193 (2020). Nature 579 Fabbri, F., Bhatia, A., Mayer, A., Schlotter, B. & Kaiser, J. BCG IT spend pulse: how COVID-19 is shifting tech priorities. (2020). https://www.bcg.com/publications/2020/how-covid-19-is-shifting-big-it-spend Candelon, F., Reichert, T., Duranton, S., di Carlo, R. C. & De Bondt, M. The rise of the AI-powered company in the postcrisis world. (2020). https://www.bcg.com/en-gb/publications/2020/business-applications-artificial-intelligence-post-covid Chao, H. et al. Integrative analysis for COVID-19 patient outcome prediction. , 101844 (2021). Med. Image Anal. 67 Zhu, X. et al. Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. , 101824 (2021). Med. Image Anal. 67 Yang, D. et al. Federated semi-supervised learning for Covid region segmentation in chest ct using multi-national data from China, Italy, Japan. , 101992 (2021). Med. Image Anal. 70 Minaee, S., Kafieh, R., Sonka, M., Yazdani, S. & Jamalipour Soufi, G. Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. , 101794 (2020). Med. Image Anal. 65 COVID-19 Studies from the World Health Organization Database. (2020). https://clinicaltrials.gov/ct2/who_table ACTIV. (2020). https://www.nih.gov/research-training/medical-research-initiatives/activ Coronavirus Treatment Acceleration Program (CTAP). US Food and Drug Administration (2020). https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap Gleeson, P., Davison, A. P., Silver, R. A. & Ascoli, G. A. A commitment to open source in neuroscience. , 964–965 (2017). Neuron 96 Piwowar, H. et al. The state of OA: a large-scale analysis of the prevalence and impact of open access articles. , e4375 (2018). PeerJ. 6 European Society of Radiology (ESR). What the radiologist should know about artificial intelligence – an ESR white paper. , 44 (2019). Insights Imaging 10 Pesapane, F., Codari, M. & Sardanelli, F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. , 35 (2018). Eur. Radiol. Exp. 2 Price, W. N. 2nd & Cohen, I. G. Privacy in the age of medical big data. , 37–43 (2019). Nat. Med. 25 Liang, W. et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. , 1081–1089 (2020). JAMA Intern. Med. 180 Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. , m1328 (2020). Brit. Med. J. 369 Zhang, L. et al. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. , 1324–1329 (2020). J. Thromb. Haemost. 18 Sands, K. E. et al. Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness. (2020). https://doi.org/10.1017/ice.2020.461 American College of Radiology. CR recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 infection. (2020). https://www.acr.org/Advocacy-and-Economics/ACR-Position-Statements/Recommendations-for-Chest-Radiography-and-CT-for-Suspected-COVID19-Infection Rubin, G. D. et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. , 172–180 (2020). Radiology 296 World Health Organization. Use of chest imaging in COVID-19. (2020). https://www.who.int/publications/i/item/use-of-chest-imaging-in-covid-19 Jamil, S. et al. Diagnosis and management of COVID-19 disease. , 10 (2020). Am. J. Respir. Crit. Care Med. 201 Redmond, C. E., Nicolaou, S., Berger, F. H., Sheikh, A. M. & Patlas, M. N. Emergency radiology during the COVID-19 pandemic: The Canadian Association of Radiologists Recommendations for Practice. , 425–430 (2020). Can. Assoc. Radiologists J. 71 Buch, V. et al. Development and validation of a deep learning model for prediction of severe outcomes in suspected COVID-19 Infection. Preprint at (2021). https://arxiv.org/abs/2103.11269 Lyons, C. & Callaghan, M. The use of high-flow nasal oxygen in COVID-19. , 843–847 (2020). Anaesthesia 75 Whittle, J. S., Pavlov, I., Sacchetti, A. D., Atwood, C. & Rosenberg, M. S. Respiratory support for adult patients with COVID-19. , 95–101 (2020). J. Am. Coll. Emerg. Physicians Open 1 Ai, J., Li, Y., Zhou, X. & Zhang, W. COVID-19: treating and managing severe cases. , 370–371 (2020). Cell Res. 30 Esteva, A. et al. A guide to deep learning in healthcare. , 24–29 (2019). Nat. Med. 25 Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S. & Rubin, D. L. Putting the data before the algorithm in big data addressing personalized healthcare. , 78 (2019). NPJ Digit. Med. 2 Thrall, J. H. et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. , 504–508 (2018). J. Am. Coll. Radiol. 15 Shilo, S., Rossman, H. & Segal, E. Axes of a revolution: challenges and promises of big data in healthcare. , 29–38 (2020). Nat. Med. 26 Gao, Y. & Cui, Y. Deep transfer learning for reducing health care disparities arising from biomedical data inequality. , 5131 (2020). Nat. Commun. 11 Rieke, N. et al. The future of digital health with federated learning. , 119 (2020). NPJ Dig. Med. 3 Yang, Q., Liu, Y., Chen, T. & Tong, Y. Federated machine learning: concept and applications. , 12 (2019). ACM Trans. Intell. Syst. Technol. 10 Ma, C. et al. On safeguarding privacy and security in the framework of federated learning. , 242–248 (2020). IEEE Netw. 34 Brisimi, T. S. et al. Federated learning of predictive models from federated Electronic Health Records. , 59–67 (2018). Int. J. Med. Inform. 112 Roth, H. R. et al. Federated learning for breast density classification: a real-world implementation. In , (eds. Albarqouni, S. et al.) Vol. 12,444, 181–191 (Springer International Publishing, 2020). Proc. Second MICCAI Workshop, DART 2020 and First MICCAI Workshop, DCL 2020 Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. , 12598 (2020). Sci. Rep. 10 Remedios, S. W., Butman, J. A., Landman, B. A. & Pham, D. L. in (eds Remedios, S. W. et al.) (Springer, 2020). Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers Xu, Y. et al. A collaborative online AI engine for CT-based COVID-19 diagnosis. Preprint at (2020). https://www.medrxiv.org/content/10.1101/2020.05.10.20096073v2 Raisaro, J. L. et al. SCOR: A secure international informatics infrastructure to investigate COVID-19. , 1721–1726 (2020). J. Am. Med. Inform. Assoc. 27 Vaid, A. et al. Federated learning of electronic health records to improve mortality prediction in hospitalized patients with COVID-19: machine learning approach. , e24207 (2021). JMIR Med. Inform. 9 Nino, G. et al. Pediatric lung imaging features of COVID-19: a systematic review and meta-analysis. , 252–263 (2021). Pediatr. Pulmonol. 56 Fredrikson, M., Jha, S. & Ristenpart, T. Model inversion attacks that exploit confidence information and basic countermeasures. In 1322–1333, (2015). Proc. 22nd ACM SIGSAC Conference on Computer and Communications Security https://doi.org/10.1145/2810103.2813677 Zhu, L., Liu, Z. & Han, S. in (eds Wallach, H. et al.) 14774–14784 (Curran Associates, Inc., 2019). Advances in Neural Information Processing Systems 32 Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. , 305–311 (2020). Nat. Mach. Intell. 2 Li, W. et al. in 133–141 (Springer, 2019). Privacy-Preserving Federated Brain Tumour Segmentation Shokri, R. & Shmatikov, V. Privacy-preserving deep learning. In (2015). Proc. 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) https://doi.org/10.1109/allerton.2015.7447103 Li, X. et al. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. , 101765 (2020). Med. Image Anal. 65 Estiri, H. et al. Predicting COVID-19 mortality with electronic medical records. , 15 (2021). NPJ Dig. Med. 4 Jiang, G. et al. Harmonization of detailed clinical models with clinical study data standards. , 65–74 (2015). Methods Inf. Med. 54 Yang, D. et al. in . (2019). Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation https://doi.org/10.1007/978-3-030-32245-8_1 Elsken, T., Metzen, J. H. & Hutter, F. Neural architecture search: a survey. , 1–21 (2019). J. Mach. Learning Res. 20 Yao, Q. et al. Taking human out of learning applications: a survey on automated machine learning. Preprint at (2019). https://arxiv.org/abs/1810.13306 Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In , PMLR , 448–456 (2015). Proc. 32nd International Conf. Machine Learning 37 Kaufman, S., Rosset, S. & Perlich, C. Leakage in data mining: formulation, detection, and avoidance. In , 556–563 (2011). Proc. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Zhang, C. et al. BatchCrypt: efficient homomorphic encryption for cross-silo federated learning. In , 493–506 (2020). Proc. 2020 USENIX Annual Technical Conference, ATC 2020 . (2020). Nvidia NGC Catalog: COVID-19 Related Models https://ngc.nvidia.com/catalog/models?orderBy=scoreDESC&pageNumber=0&query=covid&quickFilter=models&filters Marini, J. J. & Gattinoni, L. Management of COVID-19 respiratory distress. , 2329–2330 (2020). JAMA 323 Cook, T. M. et al. Consensus guidelines for managing the airway in patients with COVID-19: Guidelines from the Difficult Airway Society, the Association of Anaesthetists the Intensive Care Society, the Faculty of Intensive Care Medicine and the Royal College of Anaesthetist. , 785–799 (2020). Anaesthesia 75 Galloway, J. B. et al. A clinical risk score to identify patients with COVID-19 at high risk of critical care admission or death: an observational cohort study. , 282–288 (2020). J. Infect. 81 Kilaru, A. S. et al. Return hospital admissions among 1419 COVID-19 patients discharged from five U.S. emergency departments. , 1039–1042 (2020). Acad. Emerg. Med. 27 He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In (2016). Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/cvpr.2016.90 Irvin, J. et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. , 590–597 (2019). Proc. AAAI Conf. Artif. Intell. 33 Wang, R., Fu, B., Fu, G. & Wang, M. Deep & Cross network for Ad Click predictions. In Article no. 12 (2017). Proc. ADKDD’17 Abadi, M. et al. TensorFlow: asystem for large-scale machine learning. In , USENIX Association 265–283 (2016). 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) . (2020). NVIDIA Clara Imaging https://developer.nvidia.com/clara-medical-imaging Stekhoven, D. J. & Bühlmann, P. MissForest–non-parametric missing value imputation for mixed-type data. , 112–118 (2012). Bioinformatics 28 McMahan, H., Moore, E., Ramage, D., Hampson, S. & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. (2017). http://proceedings.mlr.press/v54/mcmahan17a.html Hsieh, K., Phanishayee, A., Mutlu, O. & Gibbons, P. B. The non-IID data quagmire of decentralized machine learning. In PMLR 119 (2020). Proc. 37th International Conf. Machine Learning Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. , 77 (2011). BMC Bioinformatics 12 O‘zlar MGB o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o U. Chulalongkorn University, Faculty of Medicine, Chulalongkorn University, Ratchadapisek Sompoch Endowment Fund RA (PO) (no. 001/63) o‘z klinik data, biyolojik misallar bilan bilan qaytaradi. NIHR Cambridge Biomedical Research Center, Taiwan, NIHR (Cambridge Biomedical Research Centre at Cambridge University Hospitals NHS Foundation Trust). National Taiwan University MeDA Lab and the MAHC and Taiwan National Health Insurance Administration thank the MOST Joint Research Center for AI technology, the All Vista Healthcare National Health Insurance Administration, Taiwan, the Ministry of Science and Technology, and Taiwan National Center for Theoretical Sciences Mathematics Division. National Institutes of Health (HNI) o‘z bilan bilan NIH Research Medical Scholars Program – NIH-Philish https://data.ucsf.edu/covid19 O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. Bu kartlar CC by 4.0 Deed (Attribution 4.0 International) lissiya. O‘z o‘z natur