Cohort Definition and Phenotyping Tutorial

Faculty:
Chris Knoll, Gowtham Rao, RuiJun (Ray) Chen, Juan Banda, Joel Swerdel

This workshop is to develop better approaches for designing and implementing phenotypes, both rule-based heuristics and increasing the use of probabilistic phenotypes. The learning objectives and technical competencies are:

• Learn principles for cohort definition and evaluation
• Develop rule-based heuristics in ATLAS
• Apply cohort definitions to analytical use cases of: disease phenotyping, exposure definition, cohort characterization, effect estimation and prediction.
• Design predictive model-based phenotype evaluation using APHRODITE.

Course prerequisites: knowledge about CDM and vocabulary AND need to know the contents of the patient-level data you are working with. This course is very important and foundational to our ability to conduct all the types of analyses, including clinical characterization, population-level prediction, and patient-level prediction.

Videos

The following videos were recorded during the fourth annual OHDSI symposium which took place October 11-13th, 2018. These recordings were made possible by generous support from EvidNet, Deloitte, Johnson & Johnson, and Bayer.

2018 OHDSI Cohort Definition and Phenotyping Tutorial (1 of 4)
Introduction

2018 OHDSI Cohort Definition and Phenotyping Tutorial (2 of 4)
Vocabulary Basics & SQL Examples

2018 OHDSI Cohort Definition and Phenotyping Tutorial (3 of 4)
Phenotype Evaluation

2018 OHDSI Cohort Definition and Phenotyping Tutorial (4 of 4)
Cohort Work Session

Watch on YouTube

2018 OHDSI Cohort Definition and Phenotyping Tutorial (1 of 4)
2018 OHDSI Cohort Definition and Phenotyping Tutorial (2 of 4)
2018 OHDSI Cohort Definition and Phenotyping Tutorial (3 of 4)
2018 OHDSI Cohort Definition and Phenotyping Tutorial (4 of 4)

Materials

Tutorial Slides

Sponsors
evidnedeloitte-logobayer1

 

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Other Tutorials
OMOP Common Data Model and Standardized Vocabularies
Overview of the OHDSI Analysis Ecosystem
Patient-level Prediction
Data Quality