中Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.
Machine Learning for Healthcare Professionals
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作业
17 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
该课程共有4个模块
In this module, you'll learn about some independent organizations involved in evaluating algorithms, SPIRIT-AI and CONSORT-AI. These are members of the Coalition for Health IT (CHAI). You'll also refresh yourself on the development steps of an AI Algorithm, focusing primarily on where your dataset comes from.
涵盖的内容
6个视频8篇阅读材料3个作业2个讨论话题
This Module will concentrate on the value of Machine learning in analyzing patient data, including data generated by large clinical trials. You’ll learn about the benefits of subgroup analysis and take a closer look at a large randomized trial conducted by Mayo Clinic investigators. We’ll also explore the difference between correlation and causality, including some unexpected insights about their relationship.
涵盖的内容
6个视频3篇阅读材料3个作业1个讨论话题
In this module we’ll examine the challenge of delivering AI based algorithms in the real world. What works in an AI Lab doesn't always translate at the bedside. With that in mind, we'll discuss the need for creating a delivery system that will help you incorporate these tools into everyday workflows.
涵盖的内容
4个视频2篇阅读材料5个作业1个讨论话题
In this module, you’ll gaze into our crystal ball to figure out which AI tools are likely to endure. You’ll take a closer look at conversational tech, generative AI and several Mayo Clinic digital solutions that will transform healthcare in the next few years.
涵盖的内容
8个视频3篇阅读材料6个作业1个讨论话题
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