图 书 馆
利用机器学习抗击结核病大流行Fighting the Tuberculosis Pandemic Using Machine Learning |
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| 课程网址: | http://videolectures.net/aaai2013_bennett_fighting_tuberculosis/ |
| 主讲教师: | Kristin P. Bennett |
| 开课单位: | 伦斯勒理工学院 |
| 开课时间: | 2013-11-14 |
| 课程语种: | 英语 |
| 中文简介: |  结核病（TB）感染了世界三分之一的人口，并且是全世界第二种传染病致死的第二大原因。耐药结核病的出现仍然是持续的威胁。我们研究了机器学习方法如何帮助控制结核病。在美国，每位结核病患者都会从结核病患者分离株中常规采集结核分枝杆菌复杂细菌（Mtb）的DNA指纹，以支持结核病的追踪和控制工作。我们开发学习模型，以基于DNA指纹预测Mtb的遗传谱系。挖掘与这些遗传谱系有关的结核病患者监测数据有助于发现疾病暴发，改善结核病控制并揭示Mtb表型差异。我们将讨论基于学习和可视化的工具，以支持纽约市卫生部门在开发中控制结核病的公共卫生工作。 |
| 课程简介: | Tuberculosis (TB) infects one third of the world's population and is the second leading cause of death from a single infectious agent worldwide. The emergence of drug resistant TB remains a constant threat. We examine how machine learning methods can help control tuberculosis. DNA fingerprints of Mycobacterium tuberculosis complex bacteria (Mtb) are routinely gathered from TB patient isolates for every tuberculosis patient in the United States to support TB tracking and control efforts. We develop learning models to predict the genetic lineages of Mtb based on DNA fingerprints. Mining of tuberculosis patient surveillance data with respect to these genetic lineages helps discover outbreaks, improve TB control, and reveal Mtb phenotype differences. We discuss learning- and visualization-based tools to support public health efforts towards TB control in development for the New York City Health Department. |
| 关 键 词: | 结核病; 机器学习; 数据监测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-08：zyk |
| 最后编审: | 2021-05-10：zyk |
| 阅读次数: | 60 |