Algorithmic Bias in Machine Learning Based Delirium Prediction

no code implementations • 8 Nov 2022 • Sandhya Tripathi, Bradley A Fritz, Michael S Avidan, Yixin Chen, Christopher R King

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.

A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues

1 code implementation • 19 Jul 2021 • Mohamed Abdelhack, Jiaming Zhang, Sandhya Tripathi, Bradley A Fritz, Daniel Felsky, Michael S Avidan, Yixin Chen, Christopher R King

Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare.

Imputation

A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room

1 code implementation • 29 Jul 2019 • Zhicheng Cui, Bradley A Fritz, Christopher R King, Michael S Avidan, Yixin Chen

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.

Additive models Respiratory Failure