EAGER: Mining Heterogeneous Network Constructed from Multiple Data Sources

EAGER：挖掘多数据源构建的异构网络

• 批准号: 1650531
• 负责人: Christopher Yang
• 金额: $ 19.87万
• 依托单位: Drexel University
• 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: 2017-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1650531&HistoricalAwards=false
• 关键词: EAGER; Mining; Heterogeneous; Network; Constructed

Relying on a single data source for knowledge discovery often results in unsatisfactory performance because of the missing patterns involving other potential entities and their relationships. This is particularly important in healthcare informatics areas such as pharmacovigilance. Pharmacovigilance is an important healthcare issue due to the impact of the adverse drug reactions. It complicates patients' medical conditions, increase hospital admissions, and contribute to more morbidity and event death. In current pharmacovigilance research, most work only consider a single data source for discovering the associations between the two entities, namely drugs and adverse drug reactions. This project develops a novel framework to integrate multiple data sources, including spontaneous report systems, electronic health records, pharmaceutical databases, scientific literature, and web data, for heterogeneous network mining. Such a heterogeneous network consists of multiple entities, including drugs, adverse drug reactions, patients, diseases, and symptoms, and various types of relationships among such entities.This project extends the capability of machine learning, data analytics, and pharmacovigilance by integrating multiple data sources for pharmacovigilance applications. In particular, the inclusion of patient-centric data on the web creates insights that may not be obtained from traditional data sources mainly contributed by health professionals. The outcomes of the project include techniques for heterogeneous path mining and structural topological pattern mining on four pharmacovigilance applications, namely adverse drug reaction detection, drug-drug interaction, prescribing cascade, and phenotypic information discovery. Such techniques can also be extended for drug repositioning and off-label use identification. The result of this research is beneficial to multiple disciplines including pharmacy, medicine, public health, and computing. The integrated education plan includes incorporating the research findings into courses offered by the Master of Science program in Health Informatics. The outreach plan involves organizing workshops, conferences, and seminars to disseminate the research outcomes.

由于缺少涉及其他潜在实体及其关系的模式，依赖单一数据源进行知识发现通常会导致性能不理想。 这在药物警戒等医疗信息学领域尤其重要。 由于药物不良反应的影响，药物警戒是一个重要的医疗保健问题。 它使患者的医疗状况变得复杂，增加住院人数，并导致更多的发病率和事件死亡。 在当前的药物警戒研究中，大多数工作仅考虑单一数据源来发现两个实体（即药物和药物不良反应）之间的关联。 该项目开发了一个新颖的框架来集成多个数据源，包括自发报告系统、电子健康记录、药物数据库、科学文献和网络数据，以进行异构网络挖掘。 这种异构网络由多个实体组成，包括药物、药物不良反应、患者、疾病和症状，以及这些实体之间的各种类型的关系。该项目通过集成多个数据来扩展机器学习、数据分析和药物警戒的能力药物警戒应用的来源。 特别是，在网络上包含以患者为中心的数据可以产生从主要由卫生专业人员提供的传统数据源中无法获得的见解。 该项目的成果包括四种药物警戒应用的异质路径挖掘和结构拓扑模式挖掘技术，即药物不良反应检测、药物-药物相互作用、处方级联和表型信息发现。 此类技术还可以扩展到药物重新定位和标签外使用识别。 这项研究的结果有益于药学、医学、公共卫生和计算机等多个学科。 综合教育计划包括将研究成果纳入健康信息学理学硕士课程提供的课程中。 推广计划包括组织研讨会、会议和研讨会来传播研究成果。

项目成果

Network-Based Modeling of Sepsis: Quantification and Evaluation of Simultaneity of Organ Dysfunctions

基于网络的脓毒症建模：器官功能障碍同时性的量化和评估

• DOI: 10.1145/3307339.3342160
• 发表时间: 2019
• 期刊: Computational Biology and Health Informatics 2019
• 作者: Jazayeri, Ali;Capan, Muge;Yang, Christopher;Khoshnevisan, Farzaneh;Chi, Min;Arnold, Ryan
• 通讯作者: Arnold, Ryan

Determining Associations with Word Embedding in Heterogeneous Network for Detecting Off-Label Drug Uses

• DOI: 10.1109/ichi.2017.78
• 发表时间: 2017-08
• 期刊: 2017 IEEE International Conference on Healthcare Informatics (ICHI)
• 作者: Christopher C. Yang;Mengnan Zhao

Exploiting OHC Data with Tensor Decomposition for Off-label Drug Use Detection

• DOI: 10.1109/ichi.2018.00010
• 发表时间: 2018-01-01
• 期刊: 2018 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI)
• 作者: Zhao, Mengnan;Yang, Christopher C.
• 通讯作者: Yang, Christopher C.

Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance

• DOI: 10.1016/j.artmed.2018.07.002
• 发表时间: 2018-08
• 期刊: Artificial intelligence in medicine
• 影响因子: 7.5
• 作者: Christopher C. Yang;Haodong Yang

Enriching User Experience in Online Health Communities Through Thread Recommendations and Heterogeneous Information Network Mining

• DOI: 10.1109/tcss.2018.2879044
• 发表时间: 2018-12
• 期刊: IEEE Transactions on Computational Social Systems
• 影响因子: 5
• 作者: Christopher C. Yang;Ling Jiang