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Found 6 papers, 4 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Neural Fine-Gray: Monotonic neural networks for competing risks
Time-to-event modelling, known as survival analysis, differs from standard regression as it addresses censoring in patients who do not experience the event of interest.
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data.
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system.
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances.
Leveraging Expert Consistency to Improve Algorithmic Decision Support
However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models.
