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

健康是机器学习（ML）的一个巨大潜力领域，因为护理管理的复杂性越来越高，并且有大量的数据可用。最近的研究表明，医疗保健中的模型缺乏鲁棒性，并且在所有患者和环境中的表现并不相同。最近在一般模型鲁棒性方面的工作未能转化为健康环境，部分原因是他们没有考虑模型将用于的患者，条件和背景的多样性。该项目将创造新的方法来提高模型的鲁棒性，并使研究人员能够针对更多的道德部署。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，优先考虑健康方面的可操作模型。最终，这些进步也将有助于其他高风险领域的机器学习，如贷款、教育和法律的系统，这些领域依赖于定期收集的数据来产生见解。除了这些进步的直接和长期社会影响外，这项工作还将有助于为一个新的以本科生为中心的暑期课程奠定基础，该课程的重点是将更大，更多样化的学生引入健康机器学习。患者安全的重要性加上模型鲁棒性差限制了ML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，以确保最先进的模型是鲁棒的。该项目针对三种方法来开发健壮的健康模型：确保表示和下游模型能够承受不正确的数据关联，实现公平和健壮的模型学习，以及在测试期间增强对离群数据的事后鲁棒性。首先，针对数据错误和变化的代表性鲁棒性，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弹性模型。其次，在模型学习方面，它将改进稳定训练的算法，通过结合私人和公共数据进行临床预测任务来平衡公平性/鲁棒性权衡。第三，它将针对异常值检测的测试时间方法，并扩展预先训练的模型以覆盖少数群体。该项目将产生解决数据、学习和测试鲁棒性的方法，作为在道德上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.