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.
이 강좌는 Clinical Data Science 전문 분야의 일부입니다.
Clinical Data Models and Data Quality Assessments
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강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 4시간 필요
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 videos (Total 54 min), 4 readings, 1 quiz
9개의 동영상
Clinical Research Data Warehouses9m
Entity Relationship Diagrams (ERDs)4m
Clinical Data Models4m
Why Common Data Models?10m
A Quick Tour of a Common Data Model: i2b26m
A Quick Tour of a Common Data Model: OMOP5m
A Quick Tour of a Common Data Model: Sentinel6m
A Quick Tour of a Common Data Model: PCORNet5m
4개의 읽기 자료
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
Readings and Course Materials for Module 1
1개 연습문제
Clinical Data Models and Common Data Models30m
주
2완료하는 데 3시간 필요
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 videos (Total 59 min), 1 reading, 1 quiz
6개의 동영상
Querying MIMIC-III9m
A Deep Dive into OMOP Data Model13m
Querying OMOP12m
Comparing the MIMIC and OMOP Data Models10m
The OHDSI Community Ecosystem7m
1개의 읽기 자료
Readings and Course Materials for Module 230m
1개 연습문제
Tools: Querying Clinical Data Models30m
주
3완료하는 데 3시간 필요
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 videos (Total 53 min), 1 reading, 1 quiz
6개의 동영상
Structural versus Terminology Mapping6m
Data Profiling with White Rabbit10m
Data Mapping with the Rabbit in a Hat Tool9m
Terminology Mapping10m
Example mapping of MIMIC Patient to OMOP Person8m
1개의 읽기 자료
Readings and Course Materials for Module 3
1개 연습문제
Techniques: Extract-Transform-Load and Terminology Mapping30m
주
4완료하는 데 3시간 필요
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 videos (Total 52 min), 1 reading, 1 quiz
5개의 동영상
Data profiling for data quality assessment10m
Data quality assessment using SQL13m
Callahan and Khare rules8m
OHDSI Achilles and Achilles Heel12m
1개의 읽기 자료
Readings and Course Materials for Module 430m
1개 연습문제
Techniques: Data Quality Assessments30m
강사
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
콜로라도 대학교 정보
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.
Clinical Data Science 전문 분야 정보
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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