Towards Personalized Healthcare Without Harm via Bias Modulation

Abstract

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.