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

识别和解决缺失和偏见，以增强多模式健康数据的发现

• 批准号: 10637391
• 负责人: Pengyu Hong
• 金额: $ 40.06万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-10 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10637391
• 关键词: Address; Advanced Development; Algorithms; Area; Artificial Intelligence; Attention; Awareness; Big Data; Characteristics; Chronic; Clinical Data; Collaborations; Complex; Critical Care; Data; Data Set; Decision Making; Development; Effectiveness; Electronic Health Record; Ensure; Evaluation; Evaluation Methodology; Gender; Goals; Health; Health Personnel; Healthcare; Healthcare Systems; Hospitals; Insurance; Intensive Care Units; Knowledge Discovery; Life; Machine Learning; Massachusetts; Measurement; Medical Informatics; Medicine; Methods; Modeling; Patients; Performance; Prevalence; Process; Race; Reproducibility; Research; Retinal blind spot; Risk Factors; Statistical Methods; Techniques; Technology; Time; Training; Training Technics; Visualization; Work; biomedical informatics; clinical decision support; clinical decision-making; clinical practice; computer science; deep learning; design; equity, diversity, and inclusion; health care delivery; health data; health equity; improved; innovation; large datasets; machine learning algorithm; machine learning method; machine learning model; multimodality; novel; open source; outcome prediction; predict clinical outcome; predictive modeling; success; tool

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.

项目摘要 机器学习（尤其是深度学习）在分析电子健康记录（EHR）数据中的最新成功具有 不仅激发了利益持有人的兴奋，而且还引起了人们对潜在的不公平或有偏见的临床决策的关注 通过机器学习促进。已经提出了许多公平测量。但是，他们 低估了受保护组和未保护组的分布之间的慢性系统差异。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。在EHR数据中常见的缺失值，情况可能会更加复杂， 如果不正确处理，这会加剧不公平。在这个项目中，我们旨在制定新颖的公平评估 方法论（目标1），并将其纳入创新的机器学习模型和技术的开发中 降低偏见并提高可解释性（AIM 2）。为了更好，更公平地处理丢失的价值，我们将开发新的 机器学习模型包含可训练的内部内部缺少价值归合组件和用于训练的新算法 它们具有由我们的新公平评估方法定义的约束（AIM 3）。此外，我们将积极主动 机器学习技术可以提高荒地权益（AIM 4）。我们将评估并改善我们的新公平度量 和机器学习技术在促进临床决策的背景下（AIM 5）。来自两个的大数据集 美国最大的医疗保健系统将用于进行拟议的研究。