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Found 4 papers, 4 papers with code
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision-making.
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality.
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities.
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking
We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2. 1 dataset.
