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

在美国，数百万人患有慢性病，包括 2 型糖尿病和心力衰竭。尽快筛查患者是否患有这些疾病非常重要。这项研究旨在挖掘医疗保健数据，以找到可能患有这些疾病的患者，并开发一种模型，用于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。特别是对于少数族裔和社会经济地位较低的患者来说，需要机会性筛查，因为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：一些患者可能每年看一次，而另一些患者则每隔几天看一次。第三，必须保护患者的隐私。第四，自动生成的模型必须公平、公正，尤其是针对弱势群体。最后，当前许多强大的机器学习模型的行为就像黑匣子：只有当它们的结论能够得到解释时，这些模型才会在医疗保健和其他关键领域被采用。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，特别是少数族裔和社会经济地位较低的患者。在教育方面，这项研究将在伊利诺伊大学芝加哥分校（联邦政府指定的少数族裔服务机构）招收代表性不足的学生，并支持多元化的博士和本科生群体的跨学科发展。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的最早点，从而可以筛查患者的某种慢性病。该研究将开发新的方法来整合具有缺失值和噪声特征的异构数据；保护隐私的去识别化方法；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；探索概念层面的可解释性。稳健的评估计划是拟议研究的一个组成部分。首先，所有算法都将根据当前的机器学习方法进行评估。此外，还将采用人机交互方法，团队中的临床医生将对算法预测进行非正式和正式的评估。这项研究将揭示的方法可能适用于存在异质、不完整、可识别或有偏差的时间序列数据的其他领域，例如预测面临风险的青少年、水资源监测和支持食品安全。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

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.