Drawing reproducible conclusions from observational clinical data | AI for Good Discovery

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.

Speakers:
George Hripcsak
Chair and Vivian Beaumont Allen Professor of Biomedical Informatics
Columbia University

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Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

Видео Drawing reproducible conclusions from observational clinical data | AI for Good Discovery канала AI for Good