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Found 8 papers, 2 papers with code
Algorithmic Bias in Machine Learning Based Delirium Prediction
Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias evaluation is crucial due to the existing association between social determinants of health and delirium risk.
Deep Learning to Jointly Schema Match, Impute, and Transform Databases
Second, we demonstrate a deep learning algorithm for translation between databases.
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare.
(Un)fairness in Post-operative Complication Prediction Models
With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in high-impact clinical decision-making systems must be scrutinized.
GANs for learning from very high class conditional noisy labels
We use Generative Adversarial Networks (GANs) to design a class conditional label noise (CCN) robust scheme for binary classification.
Interpretable feature subset selection: A Shapley value based approach
For feature selection and related problems, we introduce the notion of classification game, a cooperative game, with features as players and hinge loss based characteristic function and relate a feature's contribution to Shapley value based error apportioning (SVEA) of total training error.
Attribute noise robust binary classification
In Sy-De attribute noise model, where all features could be noisy together with same probability, we show that $0$-$1$ loss ($l_{0-1}$) need not be robust but a popular surrogate, squared loss ($l_{sq}$) is.
Cost Sensitive Learning in the Presence of Symmetric Label Noise
Our computational experiments on some UCI datasets with class imbalance show that classifiers of our two schemes are on par with the existing methods and in fact better in some cases w. r. t.
