课程信息
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第 3 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Some programming experience in any language.

完成时间大约为23 小时

建议:5 weeks of study, 2-4 hours/week...

英语(English)

字幕:英语(English)

您将学到的内容有

  • Check

    Create a computational phenotyping algorithm

  • Check

    Assess algorithm performance in the context of analytic goal.

  • Check

    Create combinations of at least three data types using boolean logic

  • Check

    Explain the impact of individual data type performance on computational phenotyping.

第 3 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Some programming experience in any language.

完成时间大约为23 小时

建议:5 weeks of study, 2-4 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 2 小时

Introduction: Identifying Patient Populations

Learn about computational phenotyping and how to use the technique to identify patient populations.

...
5 个视频 (总计 23 分钟), 9 个阅读材料, 2 个测验
5 个视频
Manual Record Review: Selecting Reviewers and Records6分钟
Manual Record Review: Tools and Techniques5分钟
9 个阅读材料
Introduction to Specialization Instructors5分钟
Course Policies5分钟
Accessing Course Data and Technology Platform15分钟
Introduction to Course Example15分钟
Introduction to Manual Record Review10分钟
Methods - Selecting Reviewers10分钟
Methods - Selecting Records for Review10分钟
Methods - Creating Review Instruments and Protocols10分钟
Methods - Assessing Review Quality10分钟
2 个练习
Week 1 Practice Quiz8分钟
Week 1 Assessment16分钟
2
完成时间为 3 小时

Tools: Clinical Data Types

Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.

...
5 个视频 (总计 19 分钟), 2 个阅读材料, 2 个测验
5 个视频
Computational Phenotyping: Clinical Observations2分钟
Computational Phenotyping: Medications3分钟
2 个阅读材料
Testing Individual Data Types1 小时 30 分
Note about the Assessment2分钟
2 个练习
Programming Exercises Practice Quiz30分钟
Week 2 Assessment18分钟
3
完成时间为 3 小时

Techniques: Data Manipulations and Combinations

Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.

...
2 个视频 (总计 15 分钟), 2 个阅读材料, 2 个测验
2 个阅读材料
Data Manipulations1 小时 30 分
Data Combinations45分钟
2 个练习
Programming Exercises Practice Quiz30分钟
Week 3 Assessment25分钟
4
完成时间为 1 小时

Techniques: Algorithm Selection and Portability

Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.

...
1 个视频 (总计 4 分钟), 1 个阅读材料, 1 个测验
1 个视频
1 个阅读材料
Assessing Algorithmic Accuracy, Complexity, and Portability25分钟
1 个练习
Week 4 Assessment20分钟
4.9
2 个审阅Chevron Right

来自Identifying Patient Populations的热门评论

创建者 ABMay 13th 2019

This is a well-presented course. I highly recommend.

讲师

Avatar

Laura K. Wiley, PhD

Assistant Professor
Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus

关于 科罗拉多大学系统

The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond....

关于 Clinical Data Science 专项课程

Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist! This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data. By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data. This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment....
Clinical Data Science

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  • Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.

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