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Found 5 papers, 3 papers with code
Robust prediction under missingness shifts
In many real-world problems, the best prediction performance is achieved by models that can leverage the informative nature of a value being missing.
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce.
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
This suggests that as data from more hospitals become available for training, model robustness is likely to increase, lower-bounding robustness with the performance of the most applicable data source in the training data.
Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes
We demonstrate the model performance on $29, 229$ diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.
