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

III：媒介：协作研究：强大的大规模电子病历数据挖掘框架，用于进行个性化干预的风险分层

• 批准号: 1836938
• 负责人: Heng Huang
• 金额: $ 20.02万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836938&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Robust

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

日益海量的电子病历(EMR)数据为电子病历数据挖掘提供了前所未有的机遇，以增强个性化干预的医疗体验，改善不同疾病的风险分层，促进对疾病的了解和适当的治疗。为了解决挖掘这种大规模异质EMR的关键和挑战性问题，研究人员的目标是开发：(I)新的计算工具来自动化EMR处理，包括使用新的稳健的秩k矩阵补全方法来填充缺失值的新技术；(Ii)使用多标签多实例学习来标注非结构化自由文本EMR；(Iii)新的稀疏多视图学习模型，以集成异质EMR来预测心力衰竭(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来识别纵向模式的新方法；(V)非参数贝叶斯模型用于预测心力衰竭患者再入院的事件时间结局。稀疏多视点特征学习和稳健的多任务纵向模式发现算法在电子病历数据挖掘之外有着广泛的应用。免费传播算法的源代码实现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。该项目提供了更多的机会，为学生(包括少数族裔成员和服务不足的人群)提供基于研究的高级培训，并将研究成果纳入德克萨斯大学阿灵顿分校、达拉斯得克萨斯大学西南医学中心和南方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。