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

合作研究：SCH：乳腺癌风险评分的公平联合表示学习

• 批准号: 2205329
• 负责人: Oluwasanmi Koyejo
• 金额: $ 54.64万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205329&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; Fair; Federated

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.

随着电子健康记录（EHR）在医院和诊所的可用性，可以开发出强大的机器学习模型，以支持精确的人群健康和临床决策任务，例如疾病检测，结果预测和治疗建议。该项目为跨医院的培训模型创建了一个机器学习框架，以及将公平性纳入分布式机器学习中的新工具。该项目将嵌入这些算法创新，以评估其对现实世界中的人群健康的适用性，主要重点是解决乳腺癌的筛查和治疗差异，以及对各种医疗保健应用的其他评估。该项目将以多个学术和医疗机构的协作开发和部署结束，包括有关机器学习和联合机器学习公平性的课程开发。该项目还计划涉及来自代表性不足的小组的研究生的参与。该项目将重点介绍培训EHR模型的表示方法，在该方法中，可以使用深度学习模型培训媒介，以代表临床概念（例如诊断和药物）和患者数据。所得的嵌入向量可以输入下游应用，例如乳腺癌风险评分。该项目通过解决缓解模型和数据偏见的挑战来为解决医疗保健机器学习的公平性创造一个变革性的新方向。第一个挑战是建模偏见，因为大多数表示医疗保健中的学习算法都不考虑任何公平措施，这可能导致偏见。为此，该项目开发了一种公平的表示学习算法，可以适应各种公平指标。第二个挑战是数据偏差，因为数据的分布性质限制了所得嵌入向量的下游权益和泛化性能。该项目使用一个新的公平联合表示框架来解决数据偏见，以学习来满足公平标准的表示形式，通过在不共享患者数据的情况下共同培训跨多个站点进行培训。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NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准通过评估来支持的。

项目成果

CoPur: Certifiably Robust Collaborative Inference via Feature Purification

• 发表时间: 2022
• 作者: J. Liu

Cooperative Inverse Decision Theory for Uncertain Preferences

• 发表时间: 2023
• 作者: Zachary Robertson;Hantao Zhang;Oluwasanmi Koyejo

One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning

一项策略就足够了：使用单一策略的并行探索对于无奖励强化学习来说是近乎最优的

• 发表时间: 2023
• 期刊: Proceedings of the International Workshop on Artificial Intelligence and Statistics
• 作者: Cisneros-Velarde, Pedro;Lyu, Boxiang;Koyejo, Sanmi;Kolar, Mlanden
• 通讯作者: Kolar, Mlanden