Observational Health Data Sciences and Informatics

All ATLAS documentation has moved to GitHub. Please disregard the content below as it is legacy and kept for posterity.

ATLAS has embedded the ability to generate prediction models using machine learning methods for precision medicine and disease interception, including:

• Regularized regression
• Random forest
• k-nearest neighbors

The ATLAS prediction feature uses the R package PatientLevelPrediction that builds patient level predictive models using data in Common Data Model format.

Features:

• Takes a cohort and outcome of interest as input.
• Extracts the necessary data from a database in OMOP Common Data Model format.
• Uses a large set of covariates including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc.
• Large scale regularized regression to fit the predictive models.
• Includes function for evaluating predictive models.
• Supported outcome models are logistic, Poisson, and survival (time to event).

This feature can be accessed by clicking on the Prediction menu item; there are options for inputs in the ATLAS prediction page. Additional details can be found by viewing a video tutorial: https://youtu.be/BEukCbT8UoA. And/or review of the github repository: https://github.com/OHDSI/PatientLevelPrediction