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Found 3 papers, 1 papers with code
A causal learning framework for the analysis and interpretation of COVID-19 clinical data
We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features.
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss
Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with real-life impact.
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing.
