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.

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies

1 code implementation • 8 Oct 2020 • Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David Sontag

Reinforcement learning (RL) has the potential to significantly improve clinical decision making.

Decision Making Management +1

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes

1 code implementation • 1 Jun 2020 • Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David Sontag

In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options.

Decision Making