Towards personalized healthcare without harm via bias modulation
Published in ICLR Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions, 2025
Clinical prediction models are often personalized to target heterogeneous sub-groups by using demographic attributes such as race and gender to train the model. Traditional personalization approaches involve using demographic attributes in input features or training multiple sub-models for different population subgroups (decoupling model). However, these methods often harm the performance at the subgroup level compared to non-personalized models. This paper presents a novel personalization method to improve model performance at the sub-group level. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalized models, which could have a positive impact in healthcare and other areas that require predictive models that take sub-group information into account.
@inproceedings{
ngaha2025towards,
title={Towards personalized healthcare without harm via bias modulation},
author={Frank Ngaha and Patrik Joslin Kenfack and Ulrich A{\"\i}vodji and Samira Ebrahimi Kahou},
booktitle={Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions},
year={2025},
url={https://openreview.net/forum?id=DBQ0b5JTxk}
}