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Found 8 papers, 4 papers with code
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities.
Proxy Methods for Domain Adaptation
We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels.
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target.
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.
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.
Federated and Differentially Private Learning for Electronic Health Records
The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository.
