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

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。尽早对患者进行这些疾病的筛查是很重要的。本研究旨在挖掘医疗保健数据，以找到可能发展这些条件的患者，并开发一个模型，在与患者的接触可能与具体诊断无关的情况下进行机会性筛查。机会性筛查尤其需要少数民族和社会经济地位较低的患者，他们不太可能向初级保健提供者寻求定期护理。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，从文本到数值，从连续信号到图像。其次，记录包括在不同时间点和不同频率收集的信息：一些患者可能一年一次，而另一些则每隔几天一次。第三，必须保护患者的隐私。第四，自动导出的模型必须是公平和无偏见的，尤其是对弱势群体。最后，当前许多强大的机器学习（ML）模型表现得像黑盒：只有在得出的结论能够得到解释的情况下，这些模型才会被应用于医疗保健和其他关键领域。从社会角度来看，该项目有可能对数百万人的健康产生积极影响，特别是提高少数群体和社会经济地位较低患者的治疗效果。这项研究将在伊利诺伊大学芝加哥分校招收代表性不足的学生，这是一所联邦政府指定的少数民族服务机构，并支持不同群体的博士和本科生的跨学科发展。该项目将探索新的机器学习和自然语言处理方法，以发现时间序列数据中最早的点，在这个点上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和缺失数据来提高公平性的算法；探索概念层面的可解释性。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机器学习方法进行评估。此外，将采用“人在循环”的方法，团队中的临床医生将对算法预测提供非正式和正式的评估。本研究将揭示的方法可能适用于存在异质性、不完整、可识别或有偏差的时间序列数据的其他领域，例如预测青年风险、水资源监测和支持食品安全。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.