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

医疗保健是机器学习（ML）潜力巨大的领域，因为医疗管理日益复杂，并且可以获得大量数据。最近的研究表明，医疗保健中的模型缺乏稳健性，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卫生环境，部分原因是它们没有考虑模型将用于的患者、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，并使研究人员能够针对更合乎道德的部署进行研究。这项研究将通过关注卫生数据的细微差别和复杂性，确定数据使用和模型培训方面的改进，从而优先考虑卫生领域的可操作模型。最终，这些进步还将有助于其他高风险领域的机器学习，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产生见解。除了这些进步带来的直接和长期的社会影响之外，这项工作还将有助于为一门新的以本科生为重点的暑期课程奠定基础，该课程的重点是将更多、更多样化的学生引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鲁棒性限制了机器学习在医疗保健中的实际应用，而道德部署需要开发方法和指标来确保最先进的模型具有鲁棒性。该项目以三种方法为目标来开发健壮的健康模型：确保表示和下游模型承受不正确的数据关联，实现公平和健壮的模型学习，以及在测试期间增强对异常数据的事后健壮性。首先，针对数据误差和变化的代表性稳健性，它将通过深度度量模型中的对比自我监督，在患者亚群和护理变化中建立有弹性的模型。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人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间方法，并扩展预训练模型以覆盖少数子组。该项目将产生解决数据、学习和测试稳健性的方法，作为道德部署健康模型的关键步骤。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。

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.