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Found 10 papers, 1 papers with code
Active Learning for Decision-Making from Imbalanced Observational Data
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning
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.
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
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.
Discretizing Logged Interaction Data Biases Learning for Decision-Making
Time series data that are not measured at regular intervals are commonly discretized as a preprocessing step.
A Review of Challenges and Opportunities in Machine Learning for Health
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
Reliable Decision Support using Counterfactual Models
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.
Disease Trajectory Maps
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.
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease.
