CRII: III: Metadata-guided Imbalance-Modeling for Robust Computational Healthcare

CRII：III：元数据引导的稳健计算医疗保健不平衡建模

• 批准号: 2245920
• 负责人: Xiaolei Huang
• 金额: $ 17.5万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245920&HistoricalAwards=false
• 关键词: CRII; III; Metadata; guided; Imbalance

Imbalance naturally exists in health data from text messages to electronic health records, which dampens the reliability, robustness, and trustworthiness of building computational healthcare models. However, existing methods ignore the imbalance's fundamental causes, metadata, such as demographics (e.g., gender and age), geolocation, and data sources. For example, given two cancers in a dataset, lung and breast cancers, while lung cancer is more frequent overall and in males, breast cancer occurs less frequently than lung cancer and more frequently in females, demonstrating imbalance patterns vary across metadata (gender in this case). Metadata includes essential information to describe the diversity and imbalance natures of health data. However, few studies have considered the diverse imbalance patterns across metadata factors, which has posed urgent needs and unique challenges in promoting robust and reliable imbalance modeling. This project proposes novel learning strategies that guide imbalance modeling by metadata and incorporate the varied imbalance patterns (e.g., breast cancer frequency for males and females) into training machine learning models. The general goal is to create reliable, open-source tools that other health researchers and practitioners can easily adopt. For example, one particular project outcome will be improving the machine learning classifiers for late effect assessments of pediatric cancer treatment at the St. Jude Children's Research Hospital. Materials (e.g., publications) and education activities will raise awareness and empower decision-making for health stakeholders with actional methods of developing and deploying machine learning on imbalanced healthcare data with rich and diverse metadata, such as demographics.This project will create a novel metadata-guided imbalance-learning framework by meta-learning that can achieve reliable and robust machine learning across different metadata factors. The investigator will start with individual metadata at a time, develop novel extensions to joint imbalance learning across multiple metadata factors (e.g., gender and disease category), and propose a self-adapting weighting mechanism to balance different metadata and prevent meta-learning overfitting. Finally, the investigator will propose an unsupervised generative model to infer missing metadata attributes, which jointly works with the imbalance-learning framework. While the framework generally aims to promote model robustness, the method can also apply to demographic fairness due to its goals to achieve balance performance across demographic groups. This project will examine and evaluate the proposed framework on a variety of health data by 1) new settings on different metadata factors and 2) effects and sensitivities of metadata factors for imbalance learning. Specific deliverables include developing a novel meta-learning toolkit with broad utility and educational activities to train the next-generation computational healthcare workforce.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.

从短信到电子健康记录的健康数据中自然存在不平衡，这降低了构建计算医疗保健模型的可靠性，鲁棒性和可信度。 然而，现有的方法忽略了不平衡的根本原因，元数据，如人口统计学（例如，性别和年龄）、地理位置和数据源。例如，给定一个数据集中的两种癌症，肺癌和乳腺癌，虽然肺癌在男性中总体上更常见，但乳腺癌的发生频率低于肺癌，而女性中更常见，这表明不平衡模式在元数据（在这种情况下为性别）中存在差异。元数据包含了描述卫生数据多样性和不平衡性的基本信息。然而，很少有研究考虑到元数据因素之间的不同不平衡模式，这对促进强大和可靠的不平衡建模提出了迫切的需求和独特的挑战。该项目提出了新的学习策略，通过元数据指导不平衡建模，并结合各种不平衡模式（例如，男性和女性的乳腺癌频率）转化为训练机器学习模型。总体目标是创建其他健康研究人员和从业人员可以轻松采用的可靠的开源工具。例如，一个特定的项目成果将是改进机器学习分类器，用于圣裘德儿童研究医院儿科癌症治疗的晚期效应评估。材料（例如，该项目将通过元学习创建一个新的元数据引导的不平衡学习框架，可以在不同的元数据因素之间实现可靠和强大的机器学习。研究者将一次从单个元数据开始，开发跨多个元数据因素的联合不平衡学习的新扩展（例如，性别和疾病类别），并提出了一种自适应加权机制，以平衡不同的元数据，防止元学习过拟合。最后，研究人员将提出一个无监督的生成模型来推断缺失的元数据属性，该模型与不平衡学习框架共同工作。虽然该框架通常旨在提高模型的鲁棒性，但该方法也可以应用于人口公平性，因为其目标是在人口群体之间实现平衡性能。该项目将通过以下方式检查和评估各种健康数据的拟议框架：1）不同元数据因素的新设置; 2）元数据因素对不平衡学习的影响和敏感性。具体成果包括开发一个新的元学习工具包，具有广泛的实用性和教育活动，以培训下一代计算医疗保健劳动力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。