Identifying and addressing missingness and bias to enhance discovery from multimodal health data

项目总结 最近，机器学习(尤其是深度学习)在分析电子健康记录(EHR)数据方面取得了成功 不仅刺激了利益相关者的兴奋，还引发了人们对潜在不公平或有偏见的临床决策的担忧 在机器学习的帮助下制作。已经提出了一些公平衡量标准。然而，他们 低估了受保护群体和非受保护群体分布之间的长期系统性差异。因此， 当用于开发机器学习方法时，它们可能会恶化组内问题并抑制 训练有素的机器学习模型。情况可能因EHR数据中常见的缺失值而进一步复杂化， 如果处理不当，这将加剧不公平。在这个项目中，我们的目标是开发一种新的公平性评价方法 方法(目标1)，并将其纳入创新机器学习模型和技术的开发中，以 减少偏见，增加可解释性(目标2)。为了更好、更公平地处理缺失的值，我们将开发新的 包含可训练的进程内缺失值补偿组件和要训练的新算法的机器学习模型 根据我们新的公平评估方法(目标3)定义的约束。此外，我们还将积极发展 促进健康公平的机器学习技术(目标4)。我们将评估和改进我们新的公平衡量标准 以及促进临床决策的机器学习技术(目标5)。来自以下两个项目的大型数据集 美国最大的医疗保健系统将用于开展这项拟议的研究。

PROJECT SUMMARY Recent successes of machine learning (especially deep learning) in analyzing electronic health record (EHR) data have not only stimulated excitement in stake holders but have also raised concerns potential unfair or biased clinical decision making facilitated by machine learning. A number of fairness measurements have been proposed. However, they underappreciate the chronical systematic differences between the distributions of protected and unprotected groups. Hence, when used to develop machine learning methods, they may worsen within-group issues and dampen performance of the trained machine learning models. The situation can be further complicated by missing values that are common in EHR data, which will exacerbate unfairness if not handled properly. In this project, we aim to develop a novel fairness evaluation methodology (Aim 1) and incorporate it into the development of innovative machine learning models and techniques to reduce biases and increase interpretability (Aim 2). To better and more fairly handle missing values, we will develop new machine learning models that contain trainable in-process missing value imputation components and new algorithms to train them with constraints defined by our new fairness evaluation method (Aim 3). In addition, we will develop proactive machine learning techniques to advance heath equity (Aim 4). We will evaluate and improve our new fairness measurements and machine learning techniques in the context of facilitating clinical decision making (Aim 5). Large datasets from two of the largest US healthcare systems will be used in carrying out the proposed research.