Collaborative Research: III: Medium: Knowledge discovery from highly heterogeneous, sparse and private data in biomedical informatics

合作研究：III：中：生物医学信息学中高度异构、稀疏和私有数据的知识发现

• 批准号: 2312863
• 负责人: Mudassir Rashid
• 金额: $ 32.05万
• 依托单位: Illinois Institute of Technology
• 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: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312863&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Knowledge

In the United States, millions of people have chronic conditions, including Type 2 Diabetes and Heart Failure. It is important to screen patients for these illnesses as soon as possible. This research aims at mining health care data to find patients likely to develop these conditions and to develop a model for opportunistic screening in situations where the encounter with the patient may be unrelated to the specific diagnosis. Opportunistic screening is needed especially for minority and lower socio-economic status patients, who are less likely to seek regular care from primary care providers. This research will address many challenges. First, health records include different types of data, from text to numeric values, from continuous signals to images. Second, records comprise information collected at different timepoints, and with different frequencies: some patients may be seen once a year, and others, every few days. Third, the privacy of patients must be protected. Fourth, automatically derived models must be fair and unbiased, especially towards underprivileged groups. Finally, many powerful current Machine Learning models behave like black boxes: these models will be adopted in healthcare and other critical areas only if their conclusions can be explained. From a societal point of view, this project has the potential to positively impact the health of millions of people, and in particular, of minority and lower socio-economic status patients. As concerns education, this research will recruit underrepresented students at the University of Illinois Chicago, a federally-designated Minority-Serving Institution, and support the interdisciplinary development of a diverse cohort of PhD and undergraduate students. This project will explore new Machine Learning (ML) and Natural Language Processing approaches to uncover the earliest point in temporal sequence data, in which a patient can be screened for a certain chronic condition. The research will develop novel methods to integrate heterogeneous data, which features missing values and noise; de-identification approaches to protect privacy; new approaches to concept and temporal relation extraction; algorithms to improve fairness by addressing data heterogeneity and missing data; exploration of concept-level explainability. A robust assessment plan is an integral part of the proposed research. First, all algorithms will be evaluated according to current ML methodology. Additionally, a human-in-the-loop approach will be employed, in which the clinicians on the team will provide informal and formal evaluation of the algorithm predictions. The methods this research will uncover are likely applicable to other domains where heterogeneous, incomplete, identifiable, or biased temporal sequence data exist, for example predicting youth at risk, water resource monitoring, and supporting food safety.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.

在美国，数以百万计的人患有慢性疾病，包括2型糖尿病和心力衰竭。重要的是要尽快筛查患者治疗这些疾病。这项研究旨在挖掘医疗保健数据，以发现可能发展这些疾病的患者，并在与患者相遇可能与特定诊断无关的情况下开发一个模型进行机会筛查。需要机会筛查，特别是对于少数民族和较低的社会经济地位患者，他们不太可能从初级保健提供者那里寻求定期护理。 这项研究将解决许多挑战。首先，健康记录包括不同类型的数据，从文本到数字值，从连续信号到图像。其次，记录包括在不同的时间点收集的信息，并且有不同的频率：每年可能会看到某些患者一次，而另一些患者每隔几天就会看到一次。第三，必须保护患者的隐私。第四，自动得出的模型必须是公平且公正的，尤其是针对贫困组。最后，许多功能强大的当前机器学习模型的行为就像黑匣子一样：只有在可以解释其结论的情况下，这些模型才能在医疗保健和其他关键领域中采用。从社会的角度来看，该项目有可能对数百万人，尤其是少数族裔和较低社会经济状况患者的健康产生积极影响。作为教育，这项研究将在伊利诺伊大学芝加哥大学招募代表性不足的学生，这是一家联邦指定的少数族裔服务机构，并支持各种各样的博士学位和本科生的跨学科发展。该项目将探索新的机器学习（ML）和自然语言处理方法，以揭示时间序列数据中最早的观点，其中可以在其中筛选患者的某些慢性病。这项研究将开发新的方法来整合具有缺失值和噪声的异质数据。去识别保护隐私的方法；概念和时间关系提取的新方法；通过解决数据异质性和缺少数据来提高公平性的算法；探索概念级别的解释性。强大的评估计划是拟议研究的组成部分。首先，所有算法将根据当前的ML方法进行评估。此外，将采用一种人类的方法，在该方法中，团队中的临床医生将对算法预测进行非正式和正式评估。 The methods this research will uncover are likely applicable to other domains where heterogeneous, incomplete, identifiable, or biased temporal sequence data exist, for example predicting youth at risk, water resource monitoring, and supporting food safety.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.