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: 2312862
• 负责人: Barbara DiEugenio
• 金额: $ 87.95万
• 依托单位: University of Illinois at Chicago
• 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=2312862&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 Congestive Heart Failure. It is important to screen patients for these illnesses as soon as possible. This research aims at mining healthcare 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 (ML) 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, to boost outcomes for minority and lower socio-economic status patients. 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 ML and Natural Language Processing approaches to uncover the earliest point in temporal sequence data in which a patient can be screened for a chronic condition. The research will develop novel methods to integrate heterogeneous data, which often features missing values and noise; de-identification approaches to protect privacy; new approaches for 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，数百万人患有慢性疾病，包括2型糖尿病和充血性心力衰竭。因此，尽快对患者进行这些疾病的筛查是非常重要的。本研究旨在挖掘医疗保健数据，以发现可能发生这些疾病的患者，并在与患者的接触可能与特定诊断无关的情况下开发机会性筛查模型。特别是对于少数民族和社会经济地位较低的患者，他们不太可能从初级保健提供者那里寻求定期护理，因此需要进行筛查。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，从文本到数值，从连续信号到图像。其次，记录包括在不同时间点收集的信息，并具有不同的频率：有些患者可能一年看一次，而其他人每隔几天看一次。第三，必须保护患者的隐私。第四，自动衍生的模型必须公平和公正，特别是对弱势群体。最后，目前许多强大的机器学习（ML）模型表现得像黑匣子：只有当它们的结论可以解释时，这些模型才会被用于医疗保健和其他关键领域。从社会的角度来看，该项目有可能对数百万人的健康产生积极影响，特别是提高少数民族和社会经济地位较低的患者的结果。这项研究将招募代表性不足的学生在伊利诺伊大学芝加哥，联邦指定的少数民族服务机构，并支持博士和本科生的多元化队列的跨学科发展。该项目将探索新的机器学习和自然语言处理方法，以揭示时间序列数据中的最早点，在该点可以对患者进行慢性疾病筛查。该研究将开发新的方法来整合异构数据，这些数据通常具有缺失值和噪声;保护隐私的去识别方法;概念和时间关系提取的新方法;通过解决数据异构性和缺失数据来提高公平性的算法;概念级可解释性的探索。一个强有力的评估计划是拟议研究的一个组成部分。首先，所有算法将根据当前的ML方法进行评估。此外，将采用人在回路方法，其中团队中的临床医生将提供算法预测的非正式和正式评估。这项研究将揭示的方法可能适用于其他领域的异质性，不完整的，可识别的，或有偏见的时间序列数据存在，例如预测青少年处于危险之中，水资源监测，并支持食品safety.This奖项反映了NSF的法定使命，并已被认为是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的支持。