Suggestions for each track drawn from prior years...
Track 1:
Modeling Longitudinal Dynamics of Comorbidities
Basil Maag, Stefan Feuerriegel, and Mathias Kraus (ETH Zurich) ; Maytal Saar-Tsechansky (University of Texas at Austin) ; Thomas Zueger (1) Inselspital, Bern, University Hospital, University of Bern 2) ETH Zurich)
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines
Chirag Nagpal; Dennis Wei; Bhanukiran Vinzamuri; Monica Shekhar; Sara E. Berger; Subhro Das; Kush R. Varshney
Track 2:
A Comprehensive EHR Timeseries Pre-training Benchmark
Matthew McDermott (Massachusetts Institute of Technology) ; Bret Nestor (University of Toronto) ; Evan Kim (Massachusetts Institute of Technology) ; Wancong Zhang (New York University) ; Anna Goldenberg (Hospital for Sick Children, University of Toronto, Vector Institute) ; Peter Szolovits (MIT) ; Marzyeh Ghassemi (University of Toronto ; Vector Institute for Artificial Intelligence)
Analyzing the role of model uncertainty for electronic health records
Michael W. Dusenberry; Dustin Tran; Edward Choi; Jonas Kemp; Jeremy Nixon; Ghassen Jerfel; Katherine Heller; Andrew M. Dai
Track 3:
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Luke Oakden-Rayner; Jared Dunnmon; Gustavo Carneiro; Christopher Re
Concept-based Model Explanations for Electronic Health Records
Diana Mincu (Google Research) ; Eric Loreaux (Google Health) ; Shaobo Hou (DeepMind) ; Sebastien Baur, Ivan Protsyuk, and Martin G Seneviratne (Google Health) ; Anne Mottram and Nenad Tomasev (DeepMind) ; Alan Karthikesalingam (Google Health) ; Jessica Schrouff (Google Research)
Suggestions for each track drawn from prior years...
Track 1:
Modeling Longitudinal Dynamics of Comorbidities
Basil Maag, Stefan Feuerriegel, and Mathias Kraus (ETH Zurich) ; Maytal Saar-Tsechansky (University of Texas at Austin) ; Thomas Zueger (1) Inselspital, Bern, University Hospital, University of Bern 2) ETH Zurich)
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines
Chirag Nagpal; Dennis Wei; Bhanukiran Vinzamuri; Monica Shekhar; Sara E. Berger; Subhro Das; Kush R. Varshney
Track 2:
A Comprehensive EHR Timeseries Pre-training Benchmark
Matthew McDermott (Massachusetts Institute of Technology) ; Bret Nestor (University of Toronto) ; Evan Kim (Massachusetts Institute of Technology) ; Wancong Zhang (New York University) ; Anna Goldenberg (Hospital for Sick Children, University of Toronto, Vector Institute) ; Peter Szolovits (MIT) ; Marzyeh Ghassemi (University of Toronto ; Vector Institute for Artificial Intelligence)
Analyzing the role of model uncertainty for electronic health records
Michael W. Dusenberry; Dustin Tran; Edward Choi; Jonas Kemp; Jeremy Nixon; Ghassen Jerfel; Katherine Heller; Andrew M. Dai
Track 3:
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Luke Oakden-Rayner; Jared Dunnmon; Gustavo Carneiro; Christopher Re
Concept-based Model Explanations for Electronic Health Records
Diana Mincu (Google Research) ; Eric Loreaux (Google Health) ; Shaobo Hou (DeepMind) ; Sebastien Baur, Ivan Protsyuk, and Martin G Seneviratne (Google Health) ; Anne Mottram and Nenad Tomasev (DeepMind) ; Alan Karthikesalingam (Google Health) ; Jessica Schrouff (Google Research)