Drawing reproducible conclusions from observational clinical data

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Drawing reproducible conclusions from observational clinical data

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    * Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay

  • Artificial Intelligence (AI) can improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. Observational Health Data Sciences and Informatics (OHDSI) is multi-stakeholder, interdisciplinary, international collaborative with a coordinating center at Columbia University.  With over 3000 researchers from 80 countries and health records on 928 million unique patients, OHDSI carries out federated studies at sufficient scale to answer questions about diagnosis and treatment.  

    This AI for Health Discovery presents current work addressing the bias inherent in medical literature by carrying out research at large scale, automating the analysis, correcting for confounding, and calibrating on residual confounding. Learn how OHDSI has produced evidence to inform hypertension treatment, COVID-19 therapy, and COVID-19 vaccine safety. 

    This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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    • Days
      Hours
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      Registration

      * Register (or log in) to the AI4G Neural Network to add this session to your agenda or watch the replay

    • Start date
      21 February 2023 at 15:00 CET Geneva | 09:00-10:30 EST, New York | 22:00-23:30 CST, Beijing
    • End date
      21 February 2023 at 16:30 CET Geneva | 09:00-10:30 EST, New York | 22:00-23:30 CST, Beijing
    • Duration
      90 minutes (including 30 minutes networking)
    • Topics
    • UN SDGs