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documentation:software:atlas:prediction [2017/10/17 13:49] jhardin created |
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- | This page will describe the use of the Prediction feature in ATLAS. | + | ===== 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 |