documentation:software:atlas:prediction
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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.
The 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).
Additional details can be found by viewing a video tutorial: https://youtu.be/BEukCbT8UoA. Or by review of materials in the github repository: https://github.com/OHDSI/PatientLevelPrediction
documentation/software/atlas/prediction.1508253673.txt.gz · Last modified: by jhardin