Collaborative Research: SCH: Fair Federated Representation Learning for Breast Cancer Risk Scoring

随着电子健康记录（EHRs）在医院和诊所的可用性，可以开发强大的机器学习模型来支持精确的人口健康和临床决策任务，如疾病检测、结果预测和治疗建议。该项目创建了一个用于跨医院培训模型的机器学习框架，以及将公平性纳入分布式机器学习的新工具。该项目将嵌入这些算法创新，以评估其对现实世界精准人口健康的适用性，主要侧重于解决乳腺癌筛查和治疗的差异，以及对各种医疗保健应用的额外评估。该项目将在多个学术和医疗机构之间进行协作开发和部署，并将包括关于机器学习公平性和联邦机器学习的课程开发。该项目还计划让来自代表性不足群体的研究生参与进来。该项目将专注于训练EHR模型的表示学习方法，其中嵌入向量可以用深度学习模型进行训练，以表示临床概念（例如诊断和药物）和患者数据。由此产生的嵌入向量可以输入到下游应用程序中，例如乳腺癌风险评分。该项目通过解决减轻模型和数据偏差的挑战，为解决医疗保健机器学习中的公平性问题创造了一个变革性的新方向。第一个挑战是建模偏差，因为医疗保健中的大多数表示学习算法不考虑任何公平度量，这可能导致有偏见的嵌入。为此，该项目开发了一种可以适应各种公平指标的公平表示学习算法。第二个挑战是数据偏差，因为数据的分布式性质限制了所得到的嵌入向量的下游公平性和泛化性能。该项目使用一个新的公平联邦表示学习框架来解决数据偏差问题，该框架通过在不共享患者数据的情况下跨多个站点进行联合训练来学习满足公平标准的表示。除了为这些方向开发算法和理论框架外，该项目还将构建和发布开放软件。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the availability of electronic health records (EHRs) in hospitals and clinics, powerful machine learning models can be developed to support precision population health and clinical decision-making tasks such as disease detection, outcome prediction, and treatment recommendation. This project creates a machine learning framework for training models across hospitals and new tools for incorporating fairness into distributed machine learning. The project will embed these algorithmic innovations to evaluate their applicability to real-world precision population health with a primary focus on addressing screening and treatment disparities in breast cancer, along with additional evaluation for various healthcare applications. This project will conclude with collaborative development and deployment across multiple academic and medical institutions and will include curriculum development on fairness in machine learning and federated machine learning. This project also plans to involve participation by graduate students from underrepresented groups.This project will focus on representation learning approaches for training EHR models, where embedding vectors can be trained with deep learning models to represent clinical concepts (e.g., diagnoses and medications) and patient data. The resulting embedding vectors can be input to the downstream applications, such as breast cancer risk scoring. This project creates a transformative new direction for addressing fairness in machine learning for healthcare by addressing the challenges of mitigating model and data biases. The first challenge is modeling bias, as most representation learning algorithms in healthcare do not consider any fairness measures, which can lead to biased embeddings. To this end, this project develops a fair representation learning algorithm that can be adapted to various fairness metrics. The second challenge is data bias, as the distributed nature of the data limits both the downstream equity and generalization performance of the resulting embedding vectors. This project addresses data bias using a new fair federated representation learning framework to learn representations that satisfy fairness criteria by training jointly across multiple sites without sharing patient data. In addition to developing the algorithmic and theoretical frameworks for these directions, this project will also build and release open software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.