CAREER: Ethical Machine Learning in Health: Robustness in Data, Learning and Deployment

职业：健康领域的道德机器学习：数据、学习和部署的稳健性

• 批准号: 2339381
• 负责人: Marzyeh Ghassemi
• 金额: $ 60万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339381&HistoricalAwards=false
• 关键词: CAREER; Ethical; Machine; Learning; Health

Health is an area of immense potential for machine learning (ML), due to the increasing complexity of care management and large volume of data becoming available. Recent work has shown that models in healthcare lack robustness, and do not perform equally well across all patients and settings. Recent work in general model robustness have failed to translate to health settings in part because they do not consider the diversity of patients, conditions, and contexts that models will be used in. This project will create new ways to improve model robustness, and empower researchers to target more ethical deployments. This research will identify improvements for data use and model training that prioritize actionable models in health, by focusing on the nuance and complexity of health data. Ultimately these advances will also contribute to machine learning in other high-stakes areas such as lending, education and legal systems, that rely on routinely collected data to generate insights. Beyond the direct and long-term societal impact of these advances, this work will help lay the foundation for a new undergraduate-focused summer course focusing on bringing a larger, and more diverse, pipeline of students into machine learning in health. The importance of patient safety combined with poor model robustness limits the practical utility of ML in healthcare, and ethical deployment requires developing methods and metrics to ensure state-of-the-art models are robust. This project targets three ways to develop robust health models: ensuring representations and downstream models withstand incorrect data associations, achieving fair and robust model learning, and enhancing post-hoc robustness to outlier data during testing. First, targeting representational robustness to data error and change, it will build resilient models across patient subpopulations and variations in care through contrastive self-supervision in deep metric models. Second, in model learning, it will improve algorithms for stable training, balancing fairness/robustness trade-offs by combining private and public data for clinical prediction tasks. Third, it will target test-time methods for outlier detection and extending pre-trained models to cover minority subgroups. The project will result in methods that address robustness in data, learning, and testing, as crucial steps toward ethically deploying health models.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.

由于护理管理的复杂性不断增加以及大量数据的可用，健康是机器学习 (ML) 潜力巨大的领域。最近的研究表明，医疗保健领域的模型缺乏稳健性，并且在所有患者和环境中表现不佳。最近关于一般模型稳健性的工作未能转化为健康环境，部分原因是它们没有考虑模型将使用的患者、条件和环境的多样性。该项目将创造新的方法来提高模型的稳健性，并赋予研究人员权力以更加道德的部署为目标。这项研究将通过关注健康数据的细微差别和复杂性，确定数据使用和模型训练的改进，优先考虑健康领域的可行模型。最终，这些进步还将有助于其他高风险领域的机器学习，例如贷款、教育和法律系统，这些领域依赖于常规收集的数据来生成见解。除了这些进步的直接和长期的社会影响之外，这项工作还将有助于为新的以本科生为中心的夏季课程奠定基础，该课程的重点是让更多、更多样化的学生进入健康领域的机器学习。患者安全的重要性加上较差的模型稳健性限制了机器学习在医疗保健中的实际应用，道德部署需要开发方法和指标以确保最先进的模型稳健。该项目的目标是通过三种方式开发稳健的健康模型：确保表示和下游模型能够承受不正确的数据关联，实现公平和稳健的模型学习，以及增强测试期间对异常数据的事后稳健性。首先，以数据错误和变化的代表性鲁棒性为目标，它将通过深度度量模型中的对比自我监督，跨患者亚群和护理变化建立弹性模型。其次，在模型学习中，它将改进稳定训练的算法，通过结合临床预测任务的私有和公共数据来平衡公平性/鲁棒性权衡。第三，它将针对异常值检测的测试时方法，并将预训练模型扩展到涵盖少数群体。该项目将产生解决数据、学习和测试稳健性的方法，作为以道德方式部署健康模型的关键步骤。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进行评估，被认为值得支持标准。