Scalable Bayesian Inference in Large Medical Databases

本项目将在医学预测问题的背景下为大规模非参数贝叶斯推理开发新的高效算法。计算技术将被应用于发现被称为病理表型的中间途径，可用于基于电子健康记录和基因组数据预测心血管疾病的表达和事件。虽然许多文章讨论了电子健康记录改善药物监测和疗效的前景，但实现这一前景所需的先进统计方法需要在高效计算技术方面进行创新，这些技术是专门为大型数据集和现代计算机体系结构设计的，包括分布式计算和处理流数据。拟议工作的贡献将导致对计算机科学和统计学的贡献，以及对更安全、更高质量的医疗保健的直接影响。可扩展贝叶斯推理的工作将引起统计学、运筹学、经济学和机器学习社区对高效推理技术的广泛兴趣。每个技术研究目标都受到大型医疗数据分析问题的启发，因此拟议的工作将通过更及时地预测药物不良事件和患者结果，对改善医疗保健产生直接影响(与NSF？美国的使命是改善国民健康、繁荣和福利)。该奖学金将允许研究人员通过在哈佛大学共同指导一门机器学习课程，指导1-2个硕士学位，从而建立她已有的教学记录。通过麻省理工学院教育研究计划的高中外展项目。

This project will develop new efficient algorithms for large-scale nonparametric Bayesian inference in the context of medical prediction problems. The computational techniques will be applied to discover intermediary pathways, known as pathophenotypes, that can be used to predict cardiovascular disease expression and events based on electronic health record and genomic data. While many articles have discussed the promise of electronic health records to improve drug surveillance and efficacy, the advanced statistical methodologies needed to realize this promise requires innovation in efficient computational techniques specially designed for large datasets and modern computer architectures including distributed computing and dealing with streaming data. The contributions of the proposed work will result both in contributions to computer science and statistics as well as in direct impacts for safer, higher-quality healthcare. The work on scalable Bayesian inference will be of general interest to wide breadth of statistics, operations research, economics, and machine learning communities working on efficient inference techniques.Each of the technical research objectives is inspired by problems in analyzing large medical data, and thus the proposed work will have direct implications for improved healthcare through more timely predictions of adverse drug events and patient outcomes (well-aligned with NSF?s mission to improve national health, prosperity and welfare). The fellowship will allow the researcher to build on her established teaching record through co-instructing a machine learning course at Harvard, supervising 1-2 masters? students on projects related to the proposed research, and high school outreach through the MIT Educational Studies Program.