Observational Health Data Sciences and Informatics

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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

This feature can be accessed by clicking on the Prediction menu item. ATLAS prediction feature uses the R package called 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).

A video tutorial is available.