Open-Source Tutorials

The open-source tools that empower OHDSI’s global research initiatives are not only available to the community, but they are also developed by the community. Leaders from around the world have developed tools that provide the foundation for OHDSI collaborators to engage in robust, reliable and reproducible observational health research.

During various OHDSI community calls, developers join and provide “10-minute tutorials” to educate the community about the tool’s potential impact, and how they can be used in research. These tutorials are available on the OHDSI YouTube channel, but are also posted below.

The half-day “Introductory Journey from Data to Evidence” tutorial from the 2023 OHDSI Symposium is available here.

Drug Utilization (Martí Català Sabaté)

Drug Utilization is an R package to summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.

Cohort Survival (Kim López Güell)

CohortSurvival is an R package to extract and summarize survival data using the OMOP common data model, to provide reliable and reproducible survival analysis capabilities.

Treatment Patterns (Maarten van Kessel)

Treatment Patterns is an R package that contains the resources for performing a treatment pathway analysis of a study population of interest in observational databases.

All of Us Research (Louisa Smith)

The goal of the allofus R package is to streamline the use of R within the All of Us Researcher Workbench.

Phoebe 2.0 (Anna Ostropolets)

PHOEBE is a recommender system that facilitates phenotype development standardization and comprehensive concept set creation. 

ARES (Frank DeFalco)

ARES is A Research Exploration System that provides a unique combination of reports and visualizations leveraging existing OHDSI tools and integrating information that was previously disparate.

Broadsea (Lee Evans)

Broadsea deploys the core OHDSI technology stack (Atlas & R Hades), using cross-platform Docker container technology.

Strategus (Anthony Sena)

Strategus is a new vision for OHDSI network studies that aims to simplify the R infrastructure requirements for network sites.

Einstein-ATLAS (Selvin Soby)

Einstein-ATLAS, developed by the team at Montefiore, is a tool to convert OHDSI/ATLAS to an enterprise self-service tool for RWD, supporting full spectrum of use-cases and data needs in an everchanging and expanding landscape.

Automated Comparator Selection
(Justin Bohn)

This tool provides a comparator ranking algorithm based on covariate similarity that can enable large-scale, automated comparative-cohort analysis.

Patient-Level Prediction (Jenna Reps)

PatientLevelPrediction is an R package for building and validating patient-level predictive models using data in the OMOP Common Data Model format.

PheKnowLator (Tiffany Callahan)

PheKnowLator is the first fully customizable knowledge graph (KG) construction framework enabling users to build complex KGs that are Semantic Web compliant and amenable to automatic Web Ontology Language (OWL) reasoning, generate contemporary property graphs, and are importable by today’s popular graph toolkits.

PheValuator (Joel Swerdel)

PatientLevelPrediction produces a large cohort of subjects each with a predicted probability for a specified health outcome of interest.

Capr (Martin Lavallee)

Capr is an R package to develop and manipulate OHDSI cohort definitions.