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 型糖尿病和充血性心力衰竭。尽快筛查患者是否患有这些疾病非常重要。 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.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（ML）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。 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.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）招募代表性不足的学生，并支持多元化博士和本科生群体的跨学科发展。 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.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的特征； de-identification approaches to protect privacy;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； exploration of concept-level explainability.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。 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.

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.