Published on in Vol 6, No 2 (2018): Apr-Jun

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Journals

  1. Goecks J, Jalili V, Heiser L, Gray J. How Machine Learning Will Transform Biomedicine. Cell 2020;181(1):92 View
  2. Li Z, Roberts K, Jiang X, Long Q. Distributed learning from multiple EHR databases: Contextual embedding models for medical events. Journal of Biomedical Informatics 2019;92:103138 View
  3. Wang Y, Wen A, Liu S, Hersh W, Bedrick S, Liu H. Test collections for electronic health record-based clinical information retrieval. JAMIA Open 2019;2(3):360 View
  4. Kapa S, Halamka J, Raskar R. Contact Tracing to Manage COVID-19 Spread—Balancing Personal Privacy and Public Health. Mayo Clinic Proceedings 2020;95(7):1320 View
  5. Kim M, Lee J, Ohno-Machado L, Jiang X. Secure and Differentially Private Logistic Regression for Horizontally Distributed Data. IEEE Transactions on Information Forensics and Security 2020;15:695 View
  6. Jia Z, Zeng X, Duan H, Lu X, Li H. A patient-similarity-based model for diagnostic prediction. International Journal of Medical Informatics 2020;135:104073 View
  7. Liu Y, Tian M, Xu C, Zhao L. Neural network feature learning based on image self-encoding. International Journal of Advanced Robotic Systems 2020;17(2):172988142092165 View
  8. Rieke N, Hancox J, Li W, Milletarì F, Roth H, Albarqouni S, Bakas S, Galtier M, Landman B, Maier-Hein K, Ourselin S, Sheller M, Summers R, Trask A, Xu D, Baust M, Cardoso M. The future of digital health with federated learning. npj Digital Medicine 2020;3(1) View
  9. Yin F, Lin Z, Kong Q, Xu Y, Li D, Theodoridis S, Cui S. FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing. IEEE Open Journal of Signal Processing 2020;1:187 View
  10. Park C, Seo S, Kang N, Ko B, Choi B, Park C, Chang D, Kim H, Kim H, Lee H, Jang J, Ye J, Jeon J, Seo J, Kim K, Jung K, Kim N, Paek S, Shin S, Yoo S, Choi Y, Kim Y, Yoon H. Artificial Intelligence in Health Care: Current Applications and Issues. Journal of Korean Medical Science 2020;35(42) View
  11. Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, Jim Zheng W, Roberts K. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review. Journal of Biomedical Informatics 2021;115:103671 View
  12. Xu J, Glicksberg B, Su C, Walker P, Bian J, Wang F. Federated Learning for Healthcare Informatics. Journal of Healthcare Informatics Research 2021;5(1):1 View
  13. Liu J, Goetz J, Sen S, Tewari A. Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data. JMIR mHealth and uHealth 2021;9(3):e23728 View
  14. Kirienko M, Sollini M, Ninatti G, Loiacono D, Giacomello E, Gozzi N, Amigoni F, Mainardi L, Lanzi P, Chiti A. Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI. European Journal of Nuclear Medicine and Molecular Imaging 2021;48(12):3791 View
  15. Feki I, Ammar S, Kessentini Y, Muhammad K. Federated learning for COVID-19 screening from Chest X-ray images. Applied Soft Computing 2021;106:107330 View
  16. Lee T, Lee J, Jun C. Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling. Information Sciences 2021;568:403 View
  17. Abdulkareem M, Petersen S. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Frontiers in Artificial Intelligence 2021;4 View
  18. Danilevicz M, Bayer P, Nestor B, Bennamoun M, Edwards D. Resources for image-based high-throughput phenotyping in crops and data sharing challenges. Plant Physiology 2021;187(2):699 View
  19. 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 2023;26(4):2271 View
  20. Zhang A, Xing L, Zou J, Wu J. Shifting machine learning for healthcare from development to deployment and from models to data. Nature Biomedical Engineering 2022;6(12):1330 View
  21. Li L, Fan Y, Tse M, Lin K. A review of applications in federated learning. Computers & Industrial Engineering 2020;149:106854 View
  22. 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
  23. Li Z, Mao F, Wu C. Can we share models if sharing data is not an option?. Patterns 2022;3(11):100603 View
  24. Ogier du Terrail J, Leopold A, Joly C, Béguier C, Andreux M, Maussion C, Schmauch B, Tramel E, Bendjebbar E, Zaslavskiy M, Wainrib G, Milder M, Gervasoni J, Guerin J, Durand T, Livartowski A, Moutet K, Gautier C, Djafar I, Moisson A, Marini C, Galtier M, Balazard F, Dubois R, Moreira J, Simon A, Drubay D, Lacroix-Triki M, Franchet C, Bataillon G, Heudel P. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nature Medicine 2023;29(1):135 View
  25. Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. Journal of Medical Internet Research 2021;23(12):e29812 View
  26. Torkzadehmahani R, Nasirigerdeh R, Blumenthal D, Kacprowski T, List M, Matschinske J, Spaeth J, Wenke N, Baumbach J. Privacy-Preserving Artificial Intelligence Techniques in Biomedicine. Methods of Information in Medicine 2022;61(S 01):e12 View
  27. Che S, Kong Z, Peng H, Sun L, Leow A, Chen Y, He L. Federated Multi-view Learning for Private Medical Data Integration and Analysis. ACM Transactions on Intelligent Systems and Technology 2022;13(4):1 View
  28. Silva P, Vinagre J, Gama J. Towards federated learning: An overview of methods and applications. WIREs Data Mining and Knowledge Discovery 2023;13(2) View
  29. Ogundokun R, Misra S, Maskeliunas R, Damasevicius R. A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology. Information 2022;13(5):263 View
  30. Sun L, Wu J. A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data. IEEE Journal of Biomedical and Health Informatics 2023;27(2):866 View
  31. Rahman K, Ahmed F, Akhter N, Hasan M, Amin R, Aziz K, Islam A, Mukta M, Islam A. Challenges, Applications and Design Aspects of Federated Learning: A Survey. IEEE Access 2021;9:124682 View
  32. 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
  33. Dasaradharami Reddy K, Gadekallu T, Doulamis A. A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics. Computational Intelligence and Neuroscience 2023;2023:1 View
  34. Jordan S, Fontaine C, Hendricks-Sturrup R. Selecting Privacy-Enhancing Technologies for Managing Health Data Use. Frontiers in Public Health 2022;10 View
  35. Hallock H, Marshall S, 't Hoen P, Nygård J, Hoorne B, Fox C, Alagaratnam S. Federated Networks for Distributed Analysis of Health Data. Frontiers in Public Health 2021;9 View
  36. Danilevicz M, Gill M, Anderson R, Batley J, Bennamoun M, Bayer P, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Frontiers in Genetics 2022;13 View
  37. Wang H, Shen H, Li F, Wu Y, Li M, Shi Z, Deng F. Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method. Electronics 2023;12(3):730 View
  38. Li Q, Wei X, Lin H, Liu Y, Chen T, Ma X. Inspecting the Running Process of Horizontal Federated Learning via Visual Analytics. IEEE Transactions on Visualization and Computer Graphics 2022;28(12):4085 View
  39. 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
  40. Yu B, Mao W, Lv Y, Zhang C, Xie Y. A survey on federated learning in data mining. WIREs Data Mining and Knowledge Discovery 2022;12(1) View
  41. 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
  42. Gkoulalas-Divanis A, Vatsalan D, Karapiperis D, Kantarcioglu M. Modern Privacy-Preserving Record Linkage Techniques: An Overview. IEEE Transactions on Information Forensics and Security 2021;16:4966 View
  43. Lopez Pineda A, Pourshafeie A, Ioannidis A, Leibold C, Chan A, Bustamante C, Frankovich J, Wojcik G. Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients. Journal of Biomedical Informatics 2021;113:103664 View
  44. Lee M, Kim S, Kim H, Lee J. Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph. Technological Forecasting and Social Change 2022;180:121718 View
  45. Darzidehkalani E, Ghasemi-rad M, van Ooijen P. Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems. Journal of the American College of Radiology 2022;19(8):969 View
  46. Huang W, Barnard A. Federated data processing and learning for collaboration in the physical sciences. Machine Learning: Science and Technology 2022;3(4):045023 View
  47. Navaz A, T. El-Kassabi H, Serhani M, Oulhaj A, Khalil K. A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine. Journal of Personalized Medicine 2022;12(5):768 View
  48. Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Serghiou S, Rader B, Ingerman A, Mellem S, Kairouz P, Nsoesie E, MacFarlane J, Vullikanti A, Marathe M, Eastham P, Brownstein J, Arcas B, Howell M, Hernandez J. Privacy-first health research with federated learning. npj Digital Medicine 2021;4(1) View
  49. Topaloglu M, Morrell E, Rajendran S, Topaloglu U. In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare. Frontiers in Artificial Intelligence 2021;4 View
  50. Chen R, Zhang Y, Dou Z, Chen F, Xie K, Wang S. Data Sharing and Privacy in Pharmaceutical Studies. Current Pharmaceutical Design 2021;27(7):911 View
  51. Palihawadana C, Wiratunga N, Wijekoon A, Kalutarage H. FedSim: Similarity guided model aggregation for Federated Learning. Neurocomputing 2022;483:432 View
  52. Stripelis D, Thompson P, Ambite J. Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings. ACM Transactions on Intelligent Systems and Technology 2022;13(5):1 View
  53. Aouedi O, Sacco A, Piamrat K, Marchetto G. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions. IEEE Journal of Biomedical and Health Informatics 2023;27(2):790 View
  54. Xu X, Peng H, Bhuiyan M, Hao Z, Liu L, Sun L, He L. Privacy-Preserving Federated Depression Detection From Multisource Mobile Health Data. IEEE Transactions on Industrial Informatics 2022;18(7):4788 View
  55. Naz S, Phan K, Chen Y. A comprehensive review of federated learning for COVID‐19 detection. International Journal of Intelligent Systems 2022;37(3):2371 View
  56. Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine 2023;158:106848 View
  57. Gong Y, Li X, Wang L. FedMBC: Personalized federated learning via mutually beneficial collaboration. Computer Communications 2023;205:108 View
  58. Li M, Lin Y, Chen H, Aparasu R. An unsupervised embedding harmonization system for privacy-preserving data mining in healthcare. IISE Transactions on Healthcare Systems Engineering 2024;14(1):1 View
  59. Wang Q, He M, Guo L, Chai H. AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration. Briefings in Bioinformatics 2023;24(5) View
  60. Xu X, Qi Z, Han X, Xu A, Geng Z, He X, Ren Y, Duo Z. Predicting anticancer drug sensitivity on distributed data sources using federated deep learning. Heliyon 2023;9(8):e18615 View
  61. Asaadi S, Martins K, Lee M, Pantoja J. Artificial intelligence for the vascular surgeon. Seminars in Vascular Surgery 2023;36(3):394 View
  62. Lee T, Kim S, Lee J, Jun C. Word2Vec-based efficient privacy-preserving shared representation learning for federated recommendation system in a cross-device setting. Information Sciences 2023;651:119728 View
  63. Petti M, Farina L. Network medicine for patients' stratification: From single‐layer to multi‐omics. WIREs Mechanisms of Disease 2023;15(6) View
  64. Rogers M, Janjua H, Walczak S, Baker M, Read M, Cios K, Velanovich V, Pietrobon R, Kuo P. Artificial Intelligence in Surgical Research: Accomplishments and Future Directions. The American Journal of Surgery 2024;230:82 View
  65. Zhang L, Xu J, Vijayakumar P, Sharma P, Ghosh U. Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System. IEEE Transactions on Network Science and Engineering 2023;10(5):2864 View
  66. Śmietanka M, Pithadia H, Treleaven P. Federated learning for privacy-preserving data access. International Journal of Data Science and Big Data Analytics 2021;1(2):1 View
  67. Xu B, Zhang Y, Fan Z, Han L, Shen Z. Patient privacy protection: Generating available medical treatment plans based on federated learning and CBR. Information & Management 2023:103908 View
  68. PHONG L, PHUONG T, WANG L, OZAWA S. Frameworks for Privacy-Preserving Federated Learning. IEICE Transactions on Information and Systems 2024;E107.D(1):2 View
  69. Malik H, Anees T. Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays. Multimedia Tools and Applications 2024 View
  70. Jiang S, Li Y, Firouzi F, Chakrabarty K. Federated clustered multi-domain learning for health monitoring. Scientific Reports 2024;14(1) View
  71. Song J, Song Z, Zhang J, Gong Y. Privacy-Preserving Identification of Cancer Subtype-Specific Driver Genes Based on Multigenomics Data with Privatedriver. Journal of Computational Biology 2024;31(2):99 View
  72. Lee T, Kim S, Lee J, Jun C. HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis. Information Sciences 2024;662:120265 View
  73. Darzi E, Sijtsema N, van Ooijen P. A comparative study of federated learning methods for COVID-19 detection. Scientific Reports 2024;14(1) View
  74. Malhotra R, Bansal A, Kessentini M. Deployment and performance monitoring of docker based federated learning framework for software defect prediction. Cluster Computing 2024 View
  75. Wang R, Qiu H, Gao H, Li C, Dong Z, Liu J. Adaptive Horizontal Federated Learning-Based Demand Response Baseline Load Estimation. IEEE Transactions on Smart Grid 2024;15(2):1659 View
  76. Fu S, Jia H, Vassilaki M, Keloth V, Dang Y, Zhou Y, Garg M, Petersen R, St Sauver J, Moon S, Wang L, Wen A, Li F, Xu H, Tao C, Fan J, Liu H, Sohn S. FedFSA: Hybrid and federated framework for functional status ascertainment across institutions. Journal of Biomedical Informatics 2024;152:104623 View
  77. Zidi I, Issaoui I, El Khediri S, Khan R. An approach based on NSGA-III algorithm for solving the multi-objective federated learning optimization problem. International Journal of Information Technology 2024;16(5):3163 View
  78. Qu Z, Ding J, Jhaveri R, Djenouri Y, Ning X, Tiwari P. FedSarah: A Novel Low-Latency Federated Learning Algorithm for Consumer-Centric Personalized Recommendation Systems. IEEE Transactions on Consumer Electronics 2024;70(1):2675 View
  79. Chai H, Huang Y, Xu L, Song X, He M, Wang Q. A decentralized federated learning-based cancer survival prediction method with privacy protection. Heliyon 2024;10(11):e31873 View
  80. Gahlan N, Sethia D. Federated learning in Emotion Recognition Systems based on physiological signals for privacy preservation: a review. Multimedia Tools and Applications 2024 View
  81. Zhang L, Xu J, Sivaraman A, Deborah Lazarus J, Sharma P, Pandi V. A Two-Stage Differential Privacy Scheme for Federated Learning Based on Edge Intelligence. IEEE Journal of Biomedical and Health Informatics 2024;28(6):3349 View
  82. Ullah F, Srivastava G, Xiao H, Ullah S, Lin J, Zhao Y. A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems With Intermittent Clients Using Medical Imaging. IEEE Journal of Biomedical and Health Informatics 2024;28(6):3293 View
  83. Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine 2024:102900 View

Books/Policy Documents

  1. Silva S, Altmann A, Gutman B, Lorenzi M. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. View
  2. Sharma S, Kesarwani A, Maheshwari S, Rai B. Federated Learning for IoT Applications. View
  3. Farjana A, Makkar A. Recent Trends in Image Processing and Pattern Recognition. View
  4. Schneider P, Xhafa F. Anomaly Detection and Complex Event Processing over IoT Data Streams. View
  5. Patra G, Bhimala K, Marndi A, Chowdhury S, Rahaman J, Nandi S, Sarkar R, Gouda K, Ramesh K, Barnwal R, Raj S, Saini A. Deep Learning. View
  6. Tarcar A. Federated Learning. View
  7. Ramesh V, S. H, Sundaram S, N. B. P, G. R. H. Handbook of Research on Design, Deployment, Automation, and Testing Strategies for 6G Mobile Core Network. View
  8. Long G, Shen T, Tan Y, Gerrard L, Clarke A, Jiang J. Humanity Driven AI. View
  9. Wang D, Wang T. Soft Computing in Data Science. View
  10. Sahoo P, Saha S, Mondal S, Chowdhury S, Gowda S. Neural Information Processing. View
  11. Ameijeiras-Rodriguez C, Rb-Silva R, Diniz J, Souza J, Freitas A. Computational Science – ICCS 2023. View
  12. Gupta M, Sharma P, Kalra R. Federated Learning and AI for Healthcare 5.0. View
  13. Kakkar A, Kumar S. Federated Learning and Privacy-Preserving in Healthcare AI. View
  14. Saadati Y, Imteaj A, Amini M. Distributed Machine Learning and Computing. View
  15. Udeji F, Sur S, Kumaravelu V, Kavitha K. Federated Learning for Digital Healthcare Systems. View