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Found 4 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.
Surgical Prediction with Interpretable Latent Representation
Given the risks and cost of surgeries, there has been significant interest in exploiting predictive models to improve perioperative care.
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.
A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room
In this paper, we propose a factored generalized additive model (F-GAM) to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual.
