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videos:overview [2017/09/14 04:43] schuemie |
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===== CDM and Vocabulary ===== | ===== CDM and Vocabulary ===== | ||
- | - nothing here yet - | + | [[https://www.ohdsi.org/common-data-model-and-extract-transform-and-load-tutorial/|Common Data Model and Extract, Transform & Load Tutorial]] (3:27:22). This video describes the CDM and its integration with the Vocabulary, and provides tips and tools for extracting, transforming, and loading your data into the CDM. |
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+ | [[https://www.ohdsi.org/ohdsi-standardized-vocabulary-tutorial-recordings/|Vocabulary]] (2:45:34). These three videos describe the Vocabulary, and how it can be explored in ATLAS. | ||
===== ATLAS ===== | ===== ATLAS ===== | ||
- | [[https://www.youtube.com/watch?v=LJ3JTSCEFI0&t=3s|Introduction to ATLAS]] (1:45). This video highlights some of the key features of ATLAS. | + | [[https://www.youtube.com/watch?v=LJ3JTSCEFI0&t=3s|Introduction to ATLAS]] (0:01:45). This video highlights some of the key features of ATLAS. |
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+ | [[https://www.ohdsi.org/ohdsi-cohort-definition-and-phenotyping-tutorial-recording/|Cohort definition and phenotyping tutorial]] (3:15:28). This video introduces the concepts of cohort definition and phenotyping, demonstrates the cohort definition tool in ATLAS, and describes more advanced approaches to phenotyping. | ||
===== OHDSI R packages ===== | ===== OHDSI R packages ===== | ||
- | [[https://youtu.be/K9_0s2Rchbo|Installing software needed to run the R packages]] (6:10). This video shows how to install the software needed to run the OHDSI R packages: R, RTools, R-Studio and Java. | + | [[https://youtu.be/K9_0s2Rchbo|Installing software needed to run the R packages]] (0:06:10). This video shows how to install the software needed to run the OHDSI R packages: R, RTools, R-Studio and Java. |
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+ | [[https://www.ohdsi.org/ohdsi-estimation-methods-tutorial-recordings/|Estimation methods]] (6:03:50). These five videos explain the concept of (population-level) effect estimation, and describe how the OHDSI tools can be used to design and execute an effect estimation study using a new-user cohort design using propensity score matching. | ||