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型糖尿病和心力衰竭。尽快对患者进行这些疾病的筛查非常重要。这项研究旨在挖掘医疗数据，以发现可能发生这些疾病的患者，并开发一种模型，用于在与患者的遭遇可能与特定诊断无关的情况下进行机会性筛查。需要机会性筛查，特别是对少数族裔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。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不同频率的信息：一些患者可能一年一次就诊，另一些患者每几天就诊一次。第三，患者的隐私必须得到保护。第四，自动派生的模型必须是公平和不偏不倚的，尤其是对弱势群体。最后，当前许多强大的机器学习模型的行为就像黑匣子：只有在其结论能够得到解释的情况下，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，特别是对少数群体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招收代表不足的学生，这是一所联邦指定的少数族裔服务机构，并支持不同学科的博士和本科生群体的发展。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，在该点中，患者可以被筛选为特定的慢性病。这项研究将开发新的方法来整合具有缺失值和噪声特征的异类数据；保护隐私的去识别方法；概念和时间关系提取的新方法；通过解决数据异质性和丢失数据来提高公平性的算法；对概念级别可解释性的探索。健全的评估计划是拟议研究的组成部分。首先，所有算法都将根据当前的ML方法论进行评估。此外，还将采用人在循环中的方法，团队中的临床医生将提供对算法预测的非正式和正式评估。这项研究将发现的方法可能适用于存在异质、不完整、可识别或有偏见的时间序列数据的其他领域，例如预测处于危险中的青少年、水资源监测和支持食品安全。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.