There are 4 tracks:

Track 1: Machine Learning
Track 2 Applications: Investigation, Evaluation, and Interpretation
Track 3 Policy: Impact, Economics, and Society
Track 4: Practice

Advances in machine learning are critical for a better understanding of health. Track 1 Machine Learning seeks contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, or identify challenges with prevalent approaches. Submissions focused more on health applications, for example establishing baselines or suggesting new evaluation metrics for assessing algorithmic advances are encouraged to submit to Track 2 instead.

While submissions should address problems relevant to health, the contributions themselves are not required to be directly applied to health. For example, authors may use synthetic datasets and experiments to demonstrate the properties of algorithms.

Authors may consider one or more machine learning sub-discipline(s) from the following list: .

Bayesian learning
Causal inference
Computer vision
Deep learning architectures
Evaluation methods
Inference
Knowledge graphs
Natural language processing
Reinforcement Learning
Representation learning
Robust learning
Structured learning
Supervised learning
Survival analysis
Time series
Transfer learning
Unsupervised learning
Explainability
Algorithmic Fairness

Authors may also consider sub-disciplines not listed here.

The goal of Track 2 Applications is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark systems. Whereas the goal of Track 1 is to select papers that show significant technical novelty, submit your work here if the contribution is more focused on solving a carefully motivated problem grounded in applications.