III: Medium: Collaborative Research: Robust Large-Scale Electronic Medical Record Data Mining Framework to Conduct Risk Stratification for Personalized Intervention

电子病历（EMR）数据量的不断增加为EMR数据挖掘提供了前所未有的机会，以增强个性化干预的医疗保健体验，改善不同疾病的风险分层，并促进对疾病和适当治疗的理解。为了解决挖掘这种大规模异构电子病历中的关键和挑战性问题，研究人员的目标是：（i）新的计算工具来自动化电子病历处理，包括使用新的鲁棒秩k矩阵完成方法填充缺失值的新技术;（ii）使用多标签多实例学习对非结构化自由文本电子病历进行注释;（iii）一种新的稀疏多视图学习模型，用于整合异构EMR以预测心力衰竭（HF）患者的再入院风险并支持个性化干预;（iv）使用高阶多任务学习识别纵向模式的新方法;（v）用于预测HF患者再入院的事件时间结果的非参数贝叶斯模型。稀疏多视图特征学习和鲁棒的多任务纵向模式发现算法在EMR数据挖掘之外有着广泛的应用。 算法的源实现的免费传播使其他研究人员能够进一步开发和应用所产生的技术。特别是，这些方法和工具预计将影响其他EMR和公共卫生研究。该项目为学生（包括少数民族成员和服务不足的人口）提供了更多的以研究为基础的高级培训机会，并将研究成果纳入阿灵顿的得克萨斯大学、达拉斯的得克萨斯大学西南医学中心和南卫理公会大学的课程。欲了解更多信息，请访问网站：http://ranger.uta.edu/~heng/NSF-III-1302675.html

The increasingly large amounts of Electronic Medical Record (EMR) data offer unprecedented opportunities for EMR data mining to enhance health care experiences for personalized intervention, improve different diseases risk stratifications, and facilitate understanding about disease and appropriate treatment. To solve the key and challenging problems in mining such large-scale heterogeneous EMRs, the investigators aim to develop: (i) new computational tools to automate the EMRs processing, including new techniques for filling in missing values using a new robust rank-k matrix completion method; (ii) annotation of unstructured free-text EMRs using multi-label multi-instance learning; (iii) a new sparse multi-view learning model to integrate heterogeneous EMRs to predict the readmission risk of Heart Failure (HF) patients and to support personalized intervention; (iv) novel methods for identifying the longitudinal patterns using high-order multi-task learning; (v) a nonparametric Bayesian model for predicting the event time outcomes of the HF patients readmission. The sparse multi-view feature learning and robust multi-task longitudinal pattern finding algorithms have a broad range of applications beyond EMR data mining. Free dissemination of source implementations of the algorithms enable other researchers to further develop and apply the resulting techniques. In particular, the methods and tools are expected to impact other EMR and public health research. This project offers enhanced opportunities for research-based advanced training of students (including members of minorities and under-served populations) and integration of research results into curricula at the University of Texas at Arlington, the University of Texas Southwestern Medical Center at Dallas, and Southern Methodist University. For further information see the web site at: http://ranger.uta.edu/~heng/NSF-III-1302675.html