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.

Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure

1 code implementation • 27 Aug 2021 • Sarah Jabbour, David Fouhey, Ella Kazerooni, Jenna Wiens, Michael W Sjoding

Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of acute respiratory failure.

BIG-bench Machine Learning Decision Making +2

Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts

no code implementations • 21 Sep 2020 • Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens

This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest.

Attribute Respiratory Failure +1