課程信息
專項課程

第 3 門課程(共 6 門)

100% 在線

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

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

Some programming experience in any language.

完成時間(小時)

完成時間大約為21 小時

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

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.

專項課程

第 3 門課程(共 6 門)

100% 在線

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

可靈活調整截止日期

根據您的日程表重置截止日期。
中級

中級

Some programming experience in any language.

完成時間(小時)

完成時間大約為21 小時

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

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(小時)
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. ...
Reading
2 個視頻 (總計 7 分鐘), 9 個閱讀材料, 2 個測驗
Video2 個視頻
Introduction to Computational Phenotyping5分鐘
Reading9 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 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
Introduction to Course Example15分鐘
Quiz2 個練習
Week 1 Practice Quiz8分鐘
Week 1 Assessment16分鐘
2
完成時間(小時)
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....
Reading
5 個視頻 (總計 19 分鐘), 2 個閱讀材料, 2 個測驗
Video5 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
Reading2 個閱讀材料
Testing Individual Data Types30分鐘
Note about the Assessment2分鐘
Quiz2 個練習
Programming Exercises Practice Quiz30分鐘
Week 2 Assessment18分鐘
3
完成時間(小時)
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....
Reading
2 個視頻 (總計 15 分鐘), 2 個閱讀材料, 2 個測驗
Video2 個視頻
Combining Multiple Data Types5分鐘
Reading2 個閱讀材料
Data Manipulations30分鐘
Data Combinations45分鐘
Quiz2 個練習
Programming Exercises Practice Quiz30分鐘
Week 3 Assessment25分鐘
4
完成時間(小時)
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....
Reading
1 個視頻 (總計 4 分鐘), 1 個閱讀材料, 1 個測驗
Video1 個視頻
Reading1 個閱讀材料
Assessing Algorithmic Accuracy, Complexity, and Portability25分鐘
Quiz1 個練習
Week 4 Assessment20分鐘

講師

Avatar

Laura K. Wiley, PhD

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

關於 University of Colorado System

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

常見問題

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • 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.

還有其他問題嗎?請訪問 學生幫助中心