===== IMPORTANT NOTE =====

All ATLAS documentation has moved to [[https://github.com/OHDSI/ATLAS/wiki|GitHub]]. Please disregard the content below as it is legacy and kept for posterity.

===== Prediction (LEGACY) =====

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. 
{{:documentation:software:atlas:atlas_plp_details.png?800|}}
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