Introduction | Data Splitting Function Code Structure | Example | Create function | Implement function | Acknowledgments

Introduction | Algorithm Code Structure | Set | Fit | Predict | VarImp | Algorithm Example | Variable importance | Acknowledgments

Introduction | Sample Function Code Structure | Example | Create function | Implement function | Acknowledgments

Introduction | Creating a model design | Model design example 1 | Model design example 2 | Model design example 3 | Running multiple models | Validating multiple models | Viewing the results | Acknowledgments

Introduction | Study specification | Problem definition 1: Stroke in atrial fibrilation patients | Problem definition 2: Angioedema in ACE inhibitor users | Study population definition | Model development settings | Example 1: Stroke in Atrial fibrillation patients | Study Specification | Study implementation | Cohort instantiation | ATLAS cohort builder | Custom cohorts | Study script creation | Data extraction | Additional inclusion criteria | Splitting the data into training/validation/testing datasets | Preprocessing the training data | Model Development | Example 2: Angioedema in ACE inhibitor users | Spliting the data into training/validation/testing datasets | Study package creation | Internal validation | Discrimination | Smooth Calibration | Other functionality | Demos | Acknowledgments | Appendix 1: Study population settings details

Introduction | Useful publication | Main steps for running a network study | Step 1 – developing the study | Step 2 – implementing the study part 1 | Step 3 – implementing the study part 2 (make sure the package is functioning as planned and the definitions are valid across sites) | Step 4 – Publication | Package Skeleton - File Structure

Introduction | Software Prerequisites | Windows Users | Mac/Linux Users | Installing the Package | Installing PatientLevelPrediction using remotes | Python environment | Installation issues | Common issues | Python environment Mac/Linux users | Acknowledgments

Introduction | Creating the learning curve | Parallel processing | Demo | Publication | Acknowledgments

Best practice publications using the OHDSI PatientLevelPrediction framework

Clinical models developed using the OHDSI PatientLevelPrediction framework

Introduction | Feature Engineering Function Code Structure | Example | Create function | Implement function | Acknowledgments

Benchmark Tasks For Large-Scale Empirical Analyses

How to use the PhenotypeLibrary R package | The full set of predictor phenotypes

Integration of GIS Data into OHDSI Model Building | Motivation | Step-by-Step Process | Step 1: Create Target & Outcome Cohorts | Step 2: Create Generic PLP Lasso Logistic Regression Model in R | Step 3: Split plpData object to train/test, augment labels with EXPOSURE_OCCURRENCE values | Step 4: Reference augmented label objects in custom feature engineering function | Step 5: Apply new train and test datasets to runPlp and evaluate output