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documentation:software:atlas:prediction [2017/10/17 15:12]
jhardin
documentation:software:atlas:prediction [2019/05/30 20:46] (current)
anthonysena
Line 1: Line 1:
 +===== 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: ATLAS has embedded the ability to generate prediction models using machine learning methods for precision medicine and disease interception,​ including:
  
-Regularized regression +  * Regularized regression 
-Random forest +  ​* ​Random forest 
-k-nearest neighbors+  ​* ​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.+The ATLAS prediction feature uses the R package PatientLevelPrediction that builds patient level predictive models using data in Common Data Model format.
  
 Features: Features:
  
-Takes a cohort and outcome of interest as input. +  * Takes a cohort and outcome of interest as input. 
-Extracts the necessary data from a database in OMOP Common Data Model format. +  ​* ​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. +  ​* ​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. +  ​* ​Large scale regularized regression to fit the predictive models. 
-Includes function for evaluating predictive models. +  ​* ​Includes function for evaluating predictive models. 
-Supported outcome models are logistic, Poisson, and survival (time to event).+  ​* ​Supported outcome models are logistic, Poisson, and survival (time to event).
  
-video tutorial ​is available: https://​github.com/​OHDSI/​PatientLevelPrediction+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 ​
  
documentation/software/atlas/prediction.1508253125.txt.gz · Last modified: 2017/10/17 15:12 by jhardin