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

基本信息

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

项目摘要

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.

随着医院和诊所使用电子健康记录 (EHR)，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，例如疾病检测、结果预测和治疗建议。该项目创建了一个用于跨医院训练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，以评估它们对现实世界精准人口健康的适用性，主要关注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额外评估。该项目将以跨多个学术和医疗机构的协作开发和部署结束，并将包括机器学习和联合机器学习公平性的课程开发。该项目还计划让来自代表性不足群体的研究生参与。该项目将重点关注用于训练 EHR 模型的表征学习方法，其中可以使用深度学习模型训练嵌入向量来表征临床概念（例如诊断和药物）和患者数据。生成的嵌入向量可以输入到下游应用程序，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的公平性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性措施，这可能导致嵌入有偏差。为此，该项目开发了一种公平表示学习算法，可以适应各种公平性指标。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嵌入向量的下游公平性和泛化性能。该项目使用新的公平联合表示学习框架来解决数据偏差，通过跨多个站点联合训练而不共享患者数据来学习满足公平标准的表示。除了开发这些方向的算法和理论框架外，该项目还将构建和发布开放软件。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

DOI：
发表时间：
2023-04
期刊：
Proceedings of the International Workshop on Artificial Intelligence and Statistics
影响因子：
0
作者：
Cisneros;Lyu, Boxiang;Koyejo, Sanmi;Kolar, Mlanden
通讯作者：
Kolar, Mlanden

CoPur: Certifiably Robust Collaborative Inference via Feature Purification

CoPur：通过特征纯化进行可证明的鲁棒协作推理

DOI：
发表时间：
2022-12
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Liu, Jing;Xie, Chulin;Koyejo, Sanmi;Li, B
通讯作者：
Li, B

Cooperative Inverse Decision Theory for Uncertain Preferences

不确定偏好的合作逆决策理论

DOI：
发表时间：
2023-04
期刊：
Proceedings of the International Workshop on Artificial Intelligence and Statistics
影响因子：
0
作者：
Robertson, Zachary;Zhang, Hantao;Koyejo, Sanmi
通讯作者：
Koyejo, Sanmi

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Oluwasanmi Koyejo其他文献

Bayesian Coresets: An Optimization Perspective

贝叶斯核心集：优化视角

DOI：
发表时间：
2020-07-01
期刊：
ArXiv
影响因子：
0
作者：
Jacky Y. Zhang;Rekha Khanna;Anastasios Kyrillidis;Oluwasanmi Koyejo
通讯作者：
Oluwasanmi Koyejo

Learning the Base Distribution in Implicit Generative Models

学习隐式生成模型中的基数分布

DOI：
10.1109/tbiom.2022.3179206
发表时间：
2018-03-12
期刊：
ArXiv
影响因子：
0
作者：
Cem Subakan;Oluwasanmi Koyejo;Paris Smaragdis
通讯作者：
Paris Smaragdis

Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle

双重血统揭秘：识别、解释

DOI：
10.48550/arxiv.2303.14151
发表时间：
2023-03-24
期刊：
ArXiv
影响因子：
0
作者：
Rylan Schaeffer;Mikail Khona;Zachary Robertson;Akhilan Boopathy;Kateryna Pistunova;J. Rocks;I. Fiete;Oluwasanmi Koyejo
通讯作者：
Oluwasanmi Koyejo