1 code implementation • 10 Apr 2019 • Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).
no code implementations • 2 Jan 2019 • Peter Schulam, Suchi Saria
To use machine learning in high stakes applications (e. g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable.
no code implementations • 11 Dec 2018 • Adarsh Subbaswamy, Peter Schulam, Suchi Saria
Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain.
no code implementations • 17 Nov 2018 • Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.
no code implementations • 6 Oct 2018 • Peter Schulam, Suchi Saria
Time series data that are not measured at regular intervals are commonly discretized as a preprocessing step.
no code implementations • 1 Jun 2018 • Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Irene Y. Chen, Rajesh Ranganath
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
no code implementations • NeurIPS 2017 • Peter Schulam, Suchi Saria
The key issue is that supervised learning algorithms are highly sensitive to the policy used to choose actions in the training data, which causes the model to capture relationships that do not generalize.
no code implementations • NeurIPS 2016 • Peter Schulam, Raman Arora
To answer these questions, we propose the Disease Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional representations of sparse and irregularly sampled time series.
no code implementations • NeurIPS 2015 • Peter Schulam, Suchi Saria
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease.