CAREER: Equitable medical decision-making

在美国，巨大的健康不平等现象依然存在。甚至在新冠肺炎大流行之前。在美国的许多地区，收入较高的人比收入最低的人多活十年。此外，大流行本身对低收入和服务不足的人群造成的打击尤其严重。有偏见的医疗决策导致了这种健康不平等。例如，以前的工作表明，一种广泛使用的健康风险预测算法评估非裔美国人患者的病情比同等患病的白人患者要轻。这项研究将通过对人类和算法做出的决定进行统计分析，使医疗决策更加公平。这项研究将找出偏见的来源(例如，当医疗检测针对的是更容易获得医疗保健的患者，而不是最有可能患病的患者)，并提出解决方案(例如，将检测重新分配给预计疾病风险最高的患者)。这不仅将使医疗保健更加公平，还可以通过将医疗资源分配到他们将发挥最大作用的地方，使其更有效率。该项目还将创建一个关于如何设计公平算法的公开课程，并就如何培训工程师设计更公平的算法，以提高工程人员的准备能力进行大规模研究。由于重要的医疗决策既由人做出，也由算法做出，该研究追求三个目标：1)检测人类医疗决策中的偏见，重点关注三个高风险的医疗设置：医疗测试分配、医疗质量评估和医学图像解释。此外，该项目还将建立算法决策辅助工具，通过提请临床医生注意他们可能忽视的医学相关特征来减少人类偏见。最后，该项目的目标是通过检查适合包含在医学算法中的特征，使算法决策更加公平。这项研究将与临床医生合作进行，以最大限度地提高患者的翻译利益。开发的方法利用贝叶斯推理和深度学习中的技术，为偏见是如何产生的提供可解释的模型，更普遍地适用于一系列高风险领域的决策--包括贷款和招聘--因此可以影响决策中与公平有关的广泛领域，包括法律和经济。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Enormous health inequality persists in the United States. Even prior to the COVID-19 pandemic. In many areas of the country, people with a higher income live up to decade longer than those in the lowest income levels. Additionally, the pandemic itself has hit low income and under-served populations especially hard. Biased medical decision-making contributes to this health inequality. For example, previous work has shown that one of widely used health risk prediction algorithms assesses African-American patients as less sick than equivalently sick White patients. This research will make medical decision-making fairer by statistically analyzing the decisions made both by humans and by algorithms. The research will identify sources of bias (for example, when medical tests are given to patients with better access to healthcare rather than to patients most likely to have a disease), and propose solutions (for example, reallocating tests to patients who are predicted to have the highest disease risk). This will not only make healthcare fairer; it can also make it more efficient, by allocating medical resources where they will do the most good. The project will also create a publicly available class on how to design fair algorithms, and conduct a large-scale study of how engineers can be trained to design fairer algorithms, to improve the preparedness of the engineering workforce.Because important medical decisions are made both by humans and by algorithms, the research pursues three objectives: 1) detecting bias in human medical decision-making, focusing on three high-stakes medical settings: allocation of medical testing, healthcare quality assessment, and interpretation of medical images. Further, the project will also build algorithmic decision-aids to reduce human bias, by drawing clinicians’ attention to medically relevant features they may have overlooked. Finally, the project targets making algorithmic decision-making more equitable, by examining the features it is appropriate to include in a medical algorithm. The research will be conducted in collaboration with clinicians to maximize translational benefit to patients. The methods developed, which draw on techniques in Bayesian inference and deep learning to provide interpretable models of how bias arises, are more generally applicable to decision-making across a host of high-stakes domains—including lending and hiring—and thus can impact a wide range of fields concerned with equity in decision-making, including law and economics.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.