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.
Ce cours fait partie de la Spécialisation Clinical Data Science
Clinical Data Models and Data Quality Assessments
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Programme du cours : ce que vous apprendrez dans ce cours
Semaine
14 heures pour terminer
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 vidéos (Total 54 min), 4 lectures, 1 quiz
9 vidéos
Clinical Research Data Warehouses9 min
Entity Relationship Diagrams (ERDs)4 min
Clinical Data Models4 min
Why Common Data Models?10 min
A Quick Tour of a Common Data Model: i2b26 min
A Quick Tour of a Common Data Model: OMOP5 min
A Quick Tour of a Common Data Model: Sentinel6 min
A Quick Tour of a Common Data Model: PCORNet5 min
4 lectures
Introduction to Specialization Instructors5 min
Course Policies5 min
Accessing Course Data and Technology Platform15 min
Readings and Course Materials for Module 12 h
1 exercice pour s'entraîner
Clinical Data Models and Common Data Models30 min
Semaine
23 heures pour terminer
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 vidéos (Total 59 min), 1 lecture, 1 quiz
6 vidéos
Querying MIMIC-III9 min
A Deep Dive into OMOP Data Model13 min
Querying OMOP12 min
Comparing the MIMIC and OMOP Data Models10 min
The OHDSI Community Ecosystem7 min
1 lecture
Readings and Course Materials for Module 21h 30min
1 exercice pour s'entraîner
Tools: Querying Clinical Data Models30 min
Semaine
33 heures pour terminer
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 vidéos (Total 53 min), 1 lecture, 1 quiz
6 vidéos
The ETL Task7 min
Structural versus Terminology Mapping6 min
Data Profiling with White Rabbit10 min
Data Mapping with the Rabbit in a Hat Tool9 min
Terminology Mapping10 min
Example mapping of MIMIC Patient to OMOP Person8 min
1 lecture
Readings and Course Materials for Module 32 h
1 exercice pour s'entraîner
Techniques: Extract-Transform-Load and Terminology Mapping30 min
Semaine
43 heures pour terminer
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 vidéos (Total 52 min), 1 lecture, 1 quiz
5 vidéos
Data profiling for data quality assessment10 min
Data quality assessment using SQL13 min
Callahan and Khare rules8 min
OHDSI Achilles and Achilles Heel12 min
1 lecture
Readings and Course Materials for Module 41h 30min
1 exercice pour s'entraîner
Techniques: Data Quality Assessments30 min
4.4
11 avisMeilleurs avis pour Clinical Data Models and Data Quality Assessments
What a great course!! Kudos to the professor for being so detail oriented!! I learned a great deal about the clinical data models from this course!!
Good instructor who took time to explain and walked through each steps of the ETL process. Highly recommended.
Enseignants
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
À propos de University of Colorado System
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À propos du Spécialisation Clinical Data Science
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.
Foire Aux Questions
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Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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