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Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.
Schuemie, Martijn; Reps, Jenna; Black, Adam; Defalco, Frank; Evans, Lee; Fridgeirsson, Egill; Gilbert, James P; Knoll, Chris; Lavallee, Martin; Rao, Gowtham A; Rijnbeek, Peter; Sadowski, Katy; Sena, Anthony; Swerdel, Joel; Williams, Ross D; Suchard, Marc.
Afiliación
  • Schuemie M; Observational Health Data Science and Informatics, New York, NY, USA.
  • Reps J; Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA.
  • Black A; Department of Biostatistics, UCLA, Los Angeles, CA, USA.
  • Defalco F; Observational Health Data Science and Informatics, New York, NY, USA.
  • Evans L; Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA.
  • Fridgeirsson E; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Gilbert JP; Observational Health Data Science and Informatics, New York, NY, USA.
  • Knoll C; Odysseus Data Services Inc., Cambridge, MA, USA.
  • Lavallee M; Observational Health Data Science and Informatics, New York, NY, USA.
  • Rao GA; Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA.
  • Rijnbeek P; Observational Health Data Science and Informatics, New York, NY, USA.
  • Sadowski K; LTS Computing LLC, West Chester, PA, USA.
  • Sena A; Observational Health Data Science and Informatics, New York, NY, USA.
  • Swerdel J; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Williams RD; Observational Health Data Science and Informatics, New York, NY, USA.
  • Suchard M; Observational Health Data Analytics, Johnson & Johnson, Titusville, NJ, USA.
Stud Health Technol Inform ; 310: 966-970, 2024 Jan 25.
Article en En | MEDLINE | ID: mdl-38269952
ABSTRACT
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Ciencia de los Datos Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Ciencia de los Datos Tipo de estudio: Guideline / Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos