Federated Learning used for predicting outcomes in SARS-COV-2 patients-Reference-Cited by-同舟云学术

Federated Learning used for predicting outcomes in SARS-COV-2 patients

Published:2021-01-08 Issue: Volume: Page:
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Author:

Flores Mona¹^ORCID,Dayan Ittai²,Roth Holger¹^ORCID,Zhong Aoxiao³,Harouni Ahmed¹,Gentili Amilcare⁴,Abidin Anas¹,Liu Andrew⁵,Costa Anthony⁶,Wood Bradford⁷,Tsai Chien-Sung⁸,Wang Chih-Hung⁹^ORCID,Hsu Chun-Nan¹⁰^ORCID,Lee CK¹,Ruan Colleen¹,Xu Daguang¹,Wu Dufan³,Huang Eddie¹,Kitamura Felipe¹¹^ORCID,Lacey Griffin¹,Corradi Gustavo César de Antônio¹²,Shin Hao-Hsin¹³,Obinata Hirofumi¹⁴,Ren Hui³,Crane Jason¹⁵,Tetreault Jesse¹,Guan Jiahui¹,Garrett John¹⁶^ORCID,Park Jung Gil¹⁷^ORCID,Dreyer Keith¹⁸,Juluru Krishna¹³^ORCID,Kersten Kristopher¹,Rockenbach Marcio Aloisio Bezerra Cavalcanti¹⁸^ORCID,Linguraru Marius¹⁹,Haider Masoom²⁰,AbdelMaseeh Meena²¹,Rieke Nicola¹,Damasceno Pablo¹⁵,Silva Pedro Mario Cruz e¹,Wang Pochuan²²^ORCID,Xu Sheng²³,Kawano Shuichi¹⁴,Sriswa Sira²⁴^ORCID,Park Soo Young²⁵,Grist Thomas²⁶,Buch Varun¹⁸,Jantarabenjakul Watsamon²⁷,Wang Weichung²⁸,Tak Won Young²⁵,Li Xiang³,Lin Xihong²⁹^ORCID,Kwon Fred⁶,Gilbert Fiona³⁰^ORCID,Kaggie Josh³¹,Li Quanzheng³,Quraini Abood¹,Feng Andrew¹,Priest Andrew³²^ORCID,Turkbey Baris³³^ORCID,Glicksberg Benjamin³⁴^ORCID,Bizzo Bernardo¹⁸^ORCID,Kim Byung Seok³⁵,Tor-Diez Carlos³⁶^ORCID,Lee Chia-Cheng³⁷,Hsu Chia-Jung³⁸,Lin Chin³⁹,Lai Chiu-Ling⁴⁰,Hess Christopher⁴¹,Compas Colin¹,Bhatia Deepi¹,Oermann Eric⁴²,Leibovitz Evan⁴³,Sasaki Hisashi¹⁴,Mori Hitoshi¹⁴,Yang Isaac¹,Sohn Jae Ho¹⁵,Murthy Krishna Nand Keshava¹³,Fu Li-Chen⁴⁴,Mendonça Matheus Ribeiro Furtado de¹¹^ORCID,Fralick Mike⁴⁵,Kang Min Kyu⁴⁶,Adil Mohammad¹,Gangai Natalie¹³,Vateekul Peerapon⁴⁷^ORCID,Elnajjar Pierre¹³,Hickman Sarah³¹,Majumdar Sharmila¹⁵,McLeod Shelley⁴⁸,Reed Sheridan²³,Graf Stefan³⁰^ORCID,Harmon Stephanie⁴⁹^ORCID,Kodama Tatsuya¹⁴,Puthanakit Thanyawee⁵⁰,Mazzulli Tony⁵¹,Lavor Vitor de Lima¹¹,Rakvongthai Yothin⁵²,Lee Yu Rim²⁵,Wen Yuhong¹

Affiliation:

1. NVIDIA

2. MGH Radiology and Harvard Medical School

3. Center for Advanced Medical Computing and Analysis, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA

4. San Diego VA Health Care System, San Diego

5. andrliu@nvidia.com

6. Mount Sinai Health System

7. Radiology & Imaging Sciences / Clinical Center, National Institutes of Health

8. Division of Cardiovascular Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.

9. Tri-Service General Hospital, National Defense Medical Center

10. Center for Research in Biological Systems, University of California, San Diego

11. Diagnósticos da América SA (Dasa)

12. GUSTAVOCORRADI@gmail.com

13. Memorial Sloan Kettering Cancer Center

14. Self-Defense Forces Central Hospital

15. Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.

16. The University of Wisconsin-Madison School of Medicine and Public Health

17. Yeungnam University College of Medicine

18. Center for Clinical Data Science, Massachusetts General Brigham, Boston, MA

19. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital and School of Medicine and Health Sciences, George Washington University, Washington, DC

20. Joint Dept. of Medical Imaging, Sinai Health System, University of Toronto, Toronto, Canada and Lunenfeld-Tanenbaum Research Institute, Toronto, Canada

21. Lunenfeld-Tanenbaum Research Institute, Toronto, Canada

22. MeDA Lab and Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan

23. Center for Interventional Oncology, National Institutes of Health, Bethesda, MD, USA

24. Chulalongkorn University

25. Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea

26. University of Wisconsin-Madison

27. Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand and Thai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bang

28. National Taiwan University

29. Harvard T.H. Chan School of Public Health

30. University of Cambridge

31. Department of Radiology, NIHR Cambridge Biomedical Resource Centre, University of Cambridge

32. Department of Radiology, NIHR Cambridge Biomedical Resource Centre, Cambridge University Hospital

33. National Institutes of Health

34. Icahn School of Medicine at Mount Sinai

35. Department of Internal Medicine, Catholic University of Daegu School of Medicine, Daegu, South Korea

36. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital , Washington, DC

37. Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C. and Division of Colorectal Surgery, Department of Surgery, Tri-Service General H

38. Planning and Management Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C.

39. School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C. and School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C. and Graduate Institute of Life Scienc

40. Medical Review and Pharmaceutical Benefits Division, National Health Insurance Administration, Taipei. Taiwan

41. University of California, San Francisco

42. NYU Langone

43. The Center for Clinical Data Science, Mass General Brigham.

44. MOST/NTU All Vista Healthcare Center, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan

45. Division of General Internal Medicine and Geriatrics (Fralick), Sinai Health System, Toronto, Canada

46. Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, South Korea

47. Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University

48. Schwartz/Reisman Emergency Medicine Institute, Sinai Health, Toronto, ON, Canada and Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada

49. National Cancer Institute

50. Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Center of Excellence in Pediatric Infectious Diseases and Vaccine, Chulalongkorn University

51. Department of Microbiology, Sinai Health/University Health Network, Toronto, Canada and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto. Canada Public Health Ontar

52. Chulalongkorn University Biomedical Imaging Group and Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

Abstract

Abstract ‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Publisher

Research Square Platform LLC

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