# Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

arXiv: https://arxiv.org/abs/1809.04663 · HTML: https://mlsanity.com/paper/1809.04663/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 23 of 23)

- [Improving the Fairness of Chest X-ray Classifiers](https://mlsanity.com/paper/2203.12609/) (2022)
- [Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems](https://mlsanity.com/paper/2103.06172/) (2021)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare](https://mlsanity.com/paper/2202.01906/) (2022)
- [A comparison of approaches to improve worst-case predictive model performance over patient subpopulations](https://mlsanity.com/paper/2108.12250/) (2021)
- [On (assessing) the fairness of risk score models](https://mlsanity.com/paper/2302.08851/) (2023)
- [Fair Representations by Compression](https://mlsanity.com/paper/2105.14044/) (2021)
- [Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit](https://mlsanity.com/paper/2305.12622/) (2023)
- [When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction](https://mlsanity.com/paper/2206.02058/) (2022)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [What is Fair? Defining Fairness in Machine Learning for Health](https://mlsanity.com/paper/2406.09307/) (2024)
- [MedEqualizer: A Framework Investigating Bias in Synthetic Medical Data and Mitigation via Augmentation](https://mlsanity.com/paper/2511.01054/) (2025)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Model Debiasing via Gradient-based Explanation on Representation](https://mlsanity.com/paper/2305.12178/) (2023)
- [Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling](https://mlsanity.com/paper/2501.13219/) (2025)
- [Critical Appraisal of Fairness Metrics in Clinical Predictive AI](https://mlsanity.com/paper/2506.17035/) (2025)
- [Can SAEs reveal and mitigate racial biases of LLMs in healthcare?](https://mlsanity.com/paper/2511.00177/) (2025)
- [SoFaiR: Single Shot Fair Representation Learning](https://mlsanity.com/paper/2204.12556/) (2022)
- [Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measures](https://mlsanity.com/paper/2206.10043/) (2022)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)
- [Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation](https://mlsanity.com/paper/2305.18160/) (2023)
- [seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness](https://mlsanity.com/paper/2504.08418/) (2025)

## Abstract (arXiv)

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance.