Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium

no code implementations • 3 Mar 2024 • Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapta, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason Fries, Parisa Rashidi, Brett Beaulieu-Jones, Xuhai Orson Xu, Matthew McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gursoy, Marzyeh Ghassemi, Emma Pierson, George Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo

The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.

Privacy-preserving patient clustering for personalized federated learning

1 code implementation • 17 Jul 2023 • Ahmed Elhussein, Gamze Gursoy

In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy.

Clustering Mortality Prediction +2

Scalable privacy-preserving cancer type prediction with homomorphic encryption

no code implementations • 12 Apr 2022 • Esha Sarkar, Eduardo Chielle, Gamze Gursoy, Leo Chen, Mark Gerstein, Michail Maniatakos

Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE).

Decision Making feature selection +3