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Health-Analytics Data to Evidence Suite (HADES): open-source software for observational research.
Open accessAbstract
The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.
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References 20 · Cited by 28
Metadata
- Authors
- Schuemie, M., Reps, J., Black, A., Defalco, F., Evans, L., Fridgeirsson, E., Gilbert, J. P., Knoll, C., Lavallee, M., Rao, G. A., Rijnbeek, P., Sadowski, K., Sena, A., Swerdel, J., Williams, R. D., & Suchard, M.
- Affiliations
- Johnson & Johnson (United States) (US)
- Observational Health Data Science and Informatics, New York, NY, USA
- Erasmus MC (NL)
- Virginia Commonwealth University (US)
- United States Department of Veterans Affairs (US)
- Corresponding author
- Schuemie, Martijn
- ORCID
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2024-01-25
- Volume
- 310
- Pages
- 966-970
- ISSN
- 0926-9630 (ISSN-L)
- 1879-8365
- PMC
- PMC10868467
- OpenAlex
- W4391224009
- Cited by
- 28
- References
- 20
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 5
- FWCI
- 18.58
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Studies in health technology and informatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by-nc)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- U.S. Department of Veterans Affairs
- European Federation of Pharmaceutical Industries and Associations (806968)
- National Institutes of Health
- National Human Genome Research Institute (R01HG006139)
- National Institute of Allergy and Infectious Diseases (R01AI153044)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- Hades, open source software, OHDSI, OMOP Common Data Model, electronic health records, administrative claims data, causal effect estimation, patient-level prediction, federated data network, data characterization, continuous integration
- MeSH terms
- Data Science, Humans, Software, Reproducibility of Results, Databases, Factual, Electronic Health Records, Observational Studies as Topic
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