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自然存在不平衡，这会抑制构建计算医疗保健模型的可靠性，鲁棒性和可信赖性。 但是，现有方法忽略了失衡的基本原因，元数据，例如人口统计学（例如性别和年龄），地理位置和数据源。例如，给定数据集，肺和乳腺癌中的两种癌症，而肺癌的整体频率更高，在男性中，乳腺癌的发生频率少于肺癌，而在女性中，乳腺癌的发生频率较低，表明整个元数据的不平衡模式都会变化（这种性别在这种情况下变化案件）。元数据包括描述健康数据的多样性和不平衡性质的基本信息。但是，很少有研究认为元数据因素之间的各种失衡模式，这在促进稳健和可靠的不平衡建模方面构成了紧急需求和独特的挑战。该项目提出了新的学习策略，该策略将元数据指导不平衡建模，并将各种失衡模式（例如，男性和女性的乳腺癌频率）纳入训练机器学习模型。一般目标是创建其他健康研究人员和从业者可以轻松采用的可靠的开源工具。例如，一个特定的项目结果将改善机器学习分类器，以在圣裘德儿童研究医院对小儿癌症治疗的晚期效果评估。材料（例如，出版物）和教育活动将通过动作使用富裕和多样化的元数据来开发和部署机器学习，以开发和部署机器学习，以促进健康利益相关者的意识并赋予决策，例如人口统计学。 - 通过元学习的指导性不平衡学习框架可以在不同的元数据因素上实现可靠且健壮的机器学习。研究者将一次从单个元数据开始，开发出对多个元数据因素（例如性别和疾病类别）的联合失衡学习的新颖扩展，并提出一种自我适应的加权机制，以平衡不同的元数据并防止元学习过度拟合。最后，研究者将提出一个无监督的生成模型来推断缺失的元数据属性，该属性与不平衡学习框架共同使用。尽管该框架通常旨在促进模型鲁棒性，但由于其目标是在人口统计组中实现平衡绩效，该方法也可以适用于人口公平。该项目将通过1）关于不同元数据因素的新环境以及2）元数据因素的影响和敏感性来检查和评估有关各种健康数据的拟议框架。特定的可交付成果包括开发一种新型的元学习工具包，并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员工。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估来评估的。标准。