PatientLevelPrediction is part of HADES.
PatientLevelPrediction is an R package for building and validating patient-level predictive models using data in the OMOP Common Data Model format.
Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. J Am Med Inform Assoc. 2018;25(8):969-975.
The figure below illustrates the prediction problem we address. Among a population at risk, we aim to predict which patients at a defined moment in time (t = 0) will experience some outcome during a time-at-risk. Prediction is done using only information about the patients in an observation window prior to that moment in time.
To define a prediction problem we have to define t=0 by a Target Cohort (T), the outcome we like to predict by an outcome cohort (O), and the time-at-risk (TAR). Furthermore, we have to make design choices for the model we like to develop, and determine the observational datasets to perform internal and external validation. This conceptual framework works for all type of prediction problems, for example those presented below (T=green, O=red).
|Calibration Plot||ROC Plot|
Demo of the Shiny Apps can be found here:
PatientLevelPrediction is an R package, with some functions implemented in C++ and python.
Requires R (version 3.3.0 or higher). Installation on Windows requires RTools. Libraries used in PatientLevelPrediction require Java and Python.
The python installation is required for some of the machine learning algorithms. We advise to install Python 3.7 using Anaconda (https://www.continuum.io/downloads).
To install the package please read the Package Installation guide
Have a look at the video below for an extensive demo of the package.
Please read the main vignette for the package:
In addition we have created vignettes that describe advanced functionality in more detail:
Package manual: PatientLevelPrediction.pdf
Documentation can be found on the package website.
PDF versions of the documentation are also available, as mentioned above.
Read here how you can contribute to this package.