III: Small: Predictive Analysis of Diabetes Dedicated Social Networks

本项目将研究糖尿病专用社交网络。 它旨在利用糖尿病患者在多个网络上的在线社交行为来预测他们的生物标志物测量结果，如糖化血红蛋白和空腹血糖。与糖尿病专用社交网络的最新研究相比，该项目将提供从探索到预测的范式转变，将大量的社会行为数据转化为有临床意义的见解和工具。该项目将通过为多模态信息提取、致密化和预测提供一套新颖的预测模型和方法来推进计算机科学。它还将通过揭示糖尿病患者的在线行为与其医疗状况之间的相互影响来促进糖尿病护理。这个项目将涉及不同层次的学生，包括研究生和本科生。项目团队计划在计算机科学和医疗保健领域的主要会议和期刊上传播该项目的研究成果。该项目将包括四个互补的研究方向。第一个目标是提取表征糖尿病患者在线社交行为的特征，包括社交内容特征和社交连接特征。第二个目标将在辅助数据的帮助下提取和推断糖尿病患者的生物标志物测量结果。第三个推力将学习将患者的在线社交行为与糖尿病生物标志物联系起来的预测函数;这个推力将从一对特征和生物标志物开始，然后通过基于张量的正则化方法联合构建多对的预测模型。第四个重点将使用来自各种来源的数据，如多个糖尿病专用社交网络，糖尿病患者的临床数据，在线百科全书数据，以及来自网络和诊所的数据该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响进行评估来支持审查标准。

This project will study diabetes dedicated social networks. It aims to harness diabetes patients' online social behaviors from multiple networks to predict their biomarker measurements such as glycated hemoglobin and fasting blood glucose. This project will provides a paradigm shift from exploration to prediction compared with state-of-the-art research on diabetes dedicated social networks, transforming the massive social behavioral data into clinically meaningful insights and tools. This project will advance computer science by providing a suite of novel predictive models and methods for multi-modality information extraction, densification, and prediction. It will also advance diabetes care by revealing the mutual impact between diabetes patients' online behaviors and their medical conditions. This project will involve students at various levels, including both graduate and undergraduate students. The project teams plans to disseminate the research outcomes from this project at major conferences and journals in both computer science and healthcare.This project will consist of four complementary research thrusts. The first thrust will extract features that characterize diabetes patients' online social behaviors, including social content features and social connectivity features. The second thrust will extract and infer diabetes patients' biomarker measurements, with the help of auxiliary data. The third thrust will learn the prediction function that connects patients' online social behaviors with diabetes biomarkers; this thrust will start from a single pair of feature and biomarker, and then jointly build the predictive models for multiple pairs via a tensor-based regularization method. The fourth thrust will thoroughly evaluate the models and methods from the previous three thrusts using data from various sources, such as multiple diabetes dedicated social networks, diabetes patients' clinical data, the online encyclopedia data, and data from online and clinic-based surveys.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.