Published on in Vol 9 , No 1 (2021) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24207, first published .
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Journals

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  2. Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, Lima I, Mancuso J, Jungmann F, Steinborn M, Saleh A, Makowski M, Rueckert D, Braren R. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence 2021;3(6):473 View
  3. Born J, Beymer D, Rajan D, Coy A, Mukherjee V, Manica M, Prasanna P, Ballah D, Guindy M, Shaham D, Shah P, Karteris E, Robertus J, Gabrani M, Rosen-Zvi M. On the role of artificial intelligence in medical imaging of COVID-19. Patterns 2021;2(6):100269 View
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  6. Sievering A, Wohlmuth P, Geßler N, Gunawardene M, Herrlinger K, Bein B, Arnold D, Bergmann M, Nowak L, Gloeckner C, Koch I, Bachmann M, Herborn C, Stang A. Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission. BMC Medical Informatics and Decision Making 2022;22(1) View
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  11. Song W, Zhang L, Liu L, Sainlaire M, Karvar M, Kang M, Pullman A, Lipsitz S, Massaro A, Patil N, Jasuja R, Dykes P. Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods. Journal of the American Medical Informatics Association 2022;29(10):1661 View
  12. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
  13. Prayitno , Shyu C, Putra K, Chen H, Tsai Y, Hossain K, Jiang W, Shae Z. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Applied Sciences 2021;11(23):11191 View
  14. Joshi M, Pal A, Sankarasubbu M. Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  15. He F, Page J, Weinberg K, Mishra A. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study. Journal of Medical Internet Research 2022;24(1):e31549 View
  16. Shin Y, Noh G, Jeong I, Chun J. Securing a Local Training Dataset Size in Federated Learning. IEEE Access 2022;10:104135 View
  17. Rahman A, Hossain M, Muhammad G, Kundu D, Debnath T, Rahman M, Khan M, Tiwari P, Band S. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster Computing 2022 View
  18. Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai C, Wang C, Hsu C, Lee C, Ruan P, Xu D, Wu D, Huang E, Kitamura F, Lacey G, de Antônio Corradi G, Nino G, Shin H, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Kaggie J, Park J, Dreyer K, Juluru K, Kersten K, Rockenbach M, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, e Silva P, Wang P, Xu S, Kawano S, Sriswasdi S, Park S, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak W, Li X, Lin X, Kwon Y, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim B, Tor-Díez C, Lee C, Hsu C, Lin C, Lai C, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn J, Murthy K, Fu L, de Mendonça M, Fralick M, Kang M, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor V, Rakvongthai Y, Lee Y, Wen Y, Gilbert F, Flores M, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine 2021;27(10):1735 View
  19. Xu D, Li T, Li Y, Su X, Tarkoma S, Jiang T, Crowcroft J, Hui P. Edge Intelligence: Empowering Intelligence to the Edge of Network. Proceedings of the IEEE 2021;109(11):1778 View
  20. Shanbehzadeh M, Haghiri H, Afrash M, Amraei M, Erfannia L, Kazemi-Arpanahi H. Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients. Shiraz E-Medical Journal 2022;23(5) View
  21. Duan S, Liu C, Han P, Jin X, Zhang X, Xiang X, Pan H, Yan X. Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning. Security and Communication Networks 2023;2023:1 View
  22. Lu S, Gao Z, Xu Q, Jiang C, Zhang A, Wang X. Class-Imbalance Privacy-Preserving Federated Learning for Decentralized Fault Diagnosis With Biometric Authentication. IEEE Transactions on Industrial Informatics 2022;18(12):9101 View
  23. Dang T, Lan X, Weng J, Feng M. Federated Learning for Electronic Health Records. ACM Transactions on Intelligent Systems and Technology 2022;13(5):1 View
  24. Bharati S, Mondal M, Podder P, Prasath V. Federated learning: Applications, challenges and future directions. International Journal of Hybrid Intelligent Systems 2022;18(1-2):19 View
  25. Oh W, Nadkarni G. Federated Learning in Health care Using Structured Medical Data. Advances in Kidney Disease and Health 2023;30(1):4 View
  26. Nguyen T, Ran A, Hu X, Yang D, Jiang M, Dou Q, Cheung C. Federated Learning in Ocular Imaging: Current Progress and Future Direction. Diagnostics 2022;12(11):2835 View
  27. Neranjan Thilakarathne N, Muneeswari G, Parthasarathy V, Alassery F, Hamam H, Kumar Mahendran R, Shafiq M. Federated Learning for Privacy-Preserved Medical Internet of Things. Intelligent Automation & Soft Computing 2022;33(1):157 View
  28. Gopukumar D, Ghoshal A, Zhao H. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach. JMIR Medical Informatics 2022;10(8):e37578 View
  29. Antunes R, André da Costa C, Küderle A, Yari I, Eskofier B. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM Transactions on Intelligent Systems and Technology 2022;13(4):1 View
  30. Nopour R, Erfannia L, Mehrabi N, Mashoufi M, Mahdavi A, Shanbehzadeh M. Comparison of Two Statistical Models for Predicting Mortality in COVID-19 Patients in Iran. Shiraz E-Medical Journal 2022;23(6) View
  31. Kumaresan M, Kumar M, Muthukumar N. Analysis of mobility based COVID-19 epidemic model using Federated Multitask Learning. Mathematical Biosciences and Engineering 2022;19(10):9983 View
  32. Alamoodi A, Zaidan B, Albahri O, Garfan S, Ahmaro I, Mohammed R, Zaidan A, Ismail A, Albahri A, Momani F, Al-Samarraay M, Jasim A, R.Q.Malik . Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. Complex & Intelligent Systems 2023 View
  33. Li H, Li C, Wang J, Yang A, Ma Z, Zhang Z, Hua D. Review on security of federated learning and its application in healthcare. Future Generation Computer Systems 2023;144:271 View
  34. Deng T, Hamdan H, Yaakob R, Kasmiran K. Personalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU. IEEE Access 2023;11:11652 View
  35. Majeed A, Zhang X, Hwang S. Applications and Challenges of Federated Learning Paradigm in the Big Data Era with Special Emphasis on COVID-19. Big Data and Cognitive Computing 2022;6(4):127 View
  36. Naseem M, Arshad H, Hashmi S, Irfan F, Ahmed F. Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network. International Journal of Medical Informatics 2021;154:104556 View
  37. Rajput A, Raman B. Privacy-Preserving Distribution and Access Control of Personalized Healthcare Data. IEEE Transactions on Industrial Informatics 2022;18(8):5584 View
  38. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. Informatics in Medicine Unlocked 2022;31:100983 View
  39. Laxmi Lydia E, Anupama C, Beno A, Elhoseny M, Alshehri M, Selim M. Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment. Soft Computing 2021 View
  40. Nopour R, Shanbezadeh M, Kazemi-Arpanahi H. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. Journal of Education and Health Promotion 2023;12(1):16 View
  41. Li W, Tong J, Anjum M, Mohammed N, Chen Y, Jiang X. Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources. BMC Medical Informatics and Decision Making 2022;22(1) View
  42. Tang P, Zheng Y, Qiu W, Wang H, Guo J, Huang Z, Liu Z. Research on Anti-Alzheimer’s Traditional Chinese Medicine with Data Security: Datasets, Methods, and Evaluation. Security and Communication Networks 2022;2022:1 View
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  44. Varzaneh Z, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Informatics in Medicine Unlocked 2022;28:100825 View
  45. Bottino F, Tagliente E, Pasquini L, Napoli A, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. Journal of Personalized Medicine 2021;11(9):893 View
  46. Wang E, Li Y, Ming R, Wei J, Du P, Zhou P, Zong S, Xiao H. The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma. Frontiers in Cell and Developmental Biology 2021;9 View
  47. Rajendran S, Xu Z, Pan W, Ghosh A, Wang F, Frasch M. Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care. PLOS Digital Health 2023;2(3):e0000117 View
  48. Chaddad A, Lu Q, Li J, Katib Y, Kateb R, Tanougast C, Bouridane A, Abdulkadir A. Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine. IEEE/CAA Journal of Automatica Sinica 2023;10(4):859 View
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  51. Lee G, Park J, Kim J, Kim Y, Choi B, Park R, Rhee S, Shin S. Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model. Healthcare Informatics Research 2023;29(2):168 View

Books/Policy Documents

  1. Wang J, Qian C, Cui S, Glass L, Ma F. Machine Learning and Knowledge Discovery in Databases. View
  2. Malla R, Katneni V. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy. View