哈佛医学院“医疗决策数据科学”课程

2019 年 2 月 4 日 专知

【导读】哈佛医学院将于2019年春季开设“医疗决策数据科学”课程，将调查当前数据和方法学方法，以进行综合高通量研究，合并基因组，暴露组学和表型数据集，以发现与疾病和健康的新关联。将向学生介绍统计决策理论以及现代数据科学和机器学习方法如何帮助改善合理的医疗决策。本文内容包含课程内容介绍、学习目标、课程日历、所需的数据集，以及相关论文。

作者 | Manri

编译 | Xiaowen

BMI 704: Data Science for Medical Decision Making

GitHub地址：

https://github.com/manrai/BMI704_Spring2019

2019 春季

课程描述

你有没有用谷歌搜索一个与健康有关的问题，并被这些命中率惊呆了？得到了实验室测试结果，并想知道它是否适用于像你这样的人？想知道你遗传的遗传变异的“优势比”是什么？解释为什么我们是谁，我们可能得到什么疾病，以及为什么我们中的一些人处于危险之中，往往不能令人满意。

现在是时候成为一个活跃的消费者了：在这个过程中，我们将培养查询大型健康数据流的技能，通过数据科学的镜头做出明智的决策。本课程将调查当前数据和方法学方法，以进行综合高通量研究，合并基因组，暴露组学和表型数据集，以发现与疾病和健康的新关联。将向学生介绍统计决策理论以及现代数据科学和机器学习方法如何帮助改善合理的医疗决策。我们鼓励学生查找公开数据（例如CDC，NIH dbGaP数据）并制定原始研究项目以提交给期刊或作为会议论文。

学习目标

培养生物医学数据科学的基本技能，包括R / RStudio，Python和基于云的基础设施。
了解决策理论和机器学习如何增强临床护理。
开发自己的预测算法，集成了暴露组学，基因组学和表型数据。
在当天的计算集群上执行数据驱动的方法。
解释已发表的文献（和新闻报道）中的统计估计和生物医学发现。

课程日历

所需数据集

论文阅读

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