This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
本課程是 Clinical Data Science 專項課程 專項課程的一部分
Clinical Data Models and Data Quality Assessments
提供方
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 4 小時
Introduction: Clinical Data Models and Common Data Models
This week describes clinical data models and explains the need for and use of common data models in national and international data networks. We will also cover the features of Entity-Relationship Diagrams (ERDs) to describe the key technical features of data models.
9 個視頻 （總計 54 分鐘）, 4 個閱讀材料, 1 個測驗
9 個視頻
Clinical Data Models4分鐘
Why Common Data Models?10分鐘
A Quick Tour of a Common Data Model: i2b26分鐘
A Quick Tour of a Common Data Model: OMOP5分鐘
A Quick Tour of a Common Data Model: Sentinel6分鐘
A Quick Tour of a Common Data Model: PCORNet5分鐘
4 個閱讀材料
Introduction to Specialization Instructors5分鐘
Course Policies5分鐘
Accessing Course Data and Technology Platform15分鐘
Readings and Course Materials for Module 12小時
1 個練習
Clinical Data Models and Common Data Models30分鐘
週
2完成時間為 3 小時
Tools: Querying Clinical Data Models
We take a deep dive into the technical features of clinical data models using MIMIC3 as our example and research common data models using OMOP as our example.
6 個視頻 （總計 59 分鐘）, 1 個閱讀材料, 1 個測驗
6 個視頻
Querying OMOP12分鐘
Comparing the MIMIC and OMOP Data Models10分鐘
The OHDSI Community Ecosystem7分鐘
1 個閱讀材料
Readings and Course Materials for Module 21 小時 30 分
1 個練習
Tools: Querying Clinical Data Models30分鐘
週
3完成時間為 3 小時
Techniques: Extract-Transform-Load and Terminology Mapping
This module teaches learners about the processes and challenges with extracting, transforming and loading (ETL) data with real-world examples in data and terminology mapping.
6 個視頻 （總計 53 分鐘）, 1 個閱讀材料, 1 個測驗
6 個視頻
The ETL Task7分鐘
Data Mapping with the Rabbit in a Hat Tool9分鐘
Terminology Mapping10分鐘
Example mapping of MIMIC Patient to OMOP Person8分鐘
1 個閱讀材料
Readings and Course Materials for Module 32小時
1 個練習
Techniques: Extract-Transform-Load and Terminology Mapping30分鐘
週
4完成時間為 3 小時
Techniques: Data Quality Assessments
We explore the dimensions of data quality by reviewing its challenges, data quality measurements used to measure it, and data quality rules to assess its acceptability for use.
5 個視頻 （總計 52 分鐘）, 1 個閱讀材料, 1 個測驗
5 個視頻
Callahan and Khare rules8分鐘
OHDSI Achilles and Achilles Heel12分鐘
1 個閱讀材料
Readings and Course Materials for Module 41 小時 30 分
1 個練習
Techniques: Data Quality Assessments30分鐘
講師
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
Michael G. Kahn, MD, PhD
Professor of Clinical InformaticsDepartment of Pediatrics, 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.
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