Webinar: AI for Sooner, Extra Exact Actual-World Analysis: Extracting Scientific Notes Knowledge at Scale
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Actual-world knowledge provides highly effective alternatives to check illness development, therapy security and effectiveness, and affected person outcomes—however a number of the most clinically significant particulars are buried in unstructured sources like scientific notes and diagnostic studies.
Truveta makes these knowledge accessible and analysis prepared. Truveta Knowledge consists of full, de-identified EHR knowledge from greater than 120 million sufferers throughout the US—together with insights from greater than 7 billion scientific notes. Scientific ideas are extracted from notes utilizing superior AI and normalized to a typical knowledge mannequin, enabling seamless integration with structured EHR knowledge and facilitating highly effective, exact analyses throughout therapeutic areas. Truveta Knowledge can also be linked with closed claims and mortality knowledge for a whole view of the affected person journey.
On this webinar, we’ll discover how the Truveta Language Mannequin (TLM)—a multi-modal AI mannequin educated on EHR knowledge—unlocks insights from scientific notes at scale. We’ll highlight cardiovascular analysis that relied on ideas like left ventricle ejection fraction (LVEF) and NYHA class to enhance classification of coronary heart failure and aortic stenosis, and to assist deeper therapy evaluation. We’ll additionally focus on how AI-extracted scientific ideas are getting used throughout therapeutic areas to speed up analysis throughout the product lifecycle.
What you’ll be taught:
- How TLM extracts scientific ideas at scale from notes, resembling LVEF, NYHA class, seizure frequency, migraine severity, ECOG standing, and extra
- How granular, normalized knowledge improves cohort definitions and outcomes analyses
- How main researchers are integrating AI-extracted ideas with EHR knowledge to uncover new insights
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