課程信息
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第 3 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

Some programming experience in any language.

完成時間大約為23 小時

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

英語(English)

字幕:英語(English)

您將學到的內容有

  • 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 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

Some programming experience in any language.

完成時間大約為23 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 2 小時

Introduction: Identifying Patient Populations

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

...
5 個視頻 (總計 23 分鐘), 9 個閱讀材料, 2 個測驗
5 個視頻
Introduction to Computational Phenotyping5分鐘
Introduction to Manual Record Review4分鐘
Manual Record Review: Selecting Reviewers and Records6分鐘
Manual Record Review: Tools and Techniques5分鐘
9 個閱讀材料
Introduction to Specialization Instructors5分鐘
Course Policies5分鐘
Accessing Course Data and Technology Platform15分鐘
Introduction to Course Example15分鐘
Introduction to Manual Record Review10分鐘
Methods - Selecting Reviewers10分鐘
Methods - Selecting Records for Review10分鐘
Methods - Creating Review Instruments and Protocols10分鐘
Methods - Assessing Review Quality10分鐘
2 個練習
Week 1 Practice Quiz8分鐘
Week 1 Assessment16分鐘
2
完成時間為 3 小時

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.

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5 個視頻 (總計 19 分鐘), 2 個閱讀材料, 2 個測驗
5 個視頻
Computational Phenotyping: Billing Data5分鐘
Computational Phenotyping: Laboratory Data3分鐘
Computational Phenotyping: Clinical Observations2分鐘
Computational Phenotyping: Medications3分鐘
2 個閱讀材料
Testing Individual Data Types1 小時 30 分
Note about the Assessment2分鐘
2 個練習
Programming Exercises Practice Quiz30分鐘
Week 2 Assessment18分鐘
3
完成時間為 3 小時

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 個視頻 (總計 15 分鐘), 2 個閱讀材料, 2 個測驗
2 個視頻
Combining Multiple Data Types5分鐘
2 個閱讀材料
Data Manipulations1 小時 30 分
Data Combinations45分鐘
2 個練習
Programming Exercises Practice Quiz30分鐘
Week 3 Assessment25分鐘
4
完成時間為 1 小時

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 個視頻 (總計 4 分鐘), 1 個閱讀材料, 1 個測驗
1 個視頻
1 個閱讀材料
Assessing Algorithmic Accuracy, Complexity, and Portability25分鐘
1 個練習
Week 4 Assessment20分鐘
5
完成時間為 4 小時

Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension

Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.

...
1 個閱讀材料, 1 個測驗
1 個閱讀材料
Welcome to Practical Applications!5分鐘
4.9
2 個審閱Chevron Right

來自Identifying Patient Populations的熱門評論

創建者 ABMay 13th 2019

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

講師

Avatar

Laura K. Wiley, PhD

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

關於 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....

關於 Clinical Data Science 專項課程

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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