Working Group Webinar Library

The dream of precision health is to develop a data-driven, continuous learning system where new health information is instantly incorporated to optimize care delivery and accelerate biomedical discovery. In reality, the health ecosystem is plagued by overwhelming unstructured data and unscalable manual processing. Self-supervised AI such as large language models (LLMs) can supercharge structuring of biomedical data and accelerate transformation towards precision health

A significant portion of data in the healthcare industry exists as narrative text, such as clinical notes in electronic health record (EHR) systems, clinical trial descriptions, biomedical literature, and consumer-generated content on social media and online forums. With the advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques, valuable insights, such as symptoms and causal relationships between medical concepts, can be extracted from various text data to aid in clinical decision-making and improve healthcare outcomes.

This session will present the work of a community of practice supported by the Gordon and Betty Moore foundation to promote the use of NLP “at-scale” in CQM used in accountability programs.

Join the Natural Language Processing Working Group for a talk from Dr. Trevor Cohen on Probing the Dimensions of Meaning in Medicine with Distributed Representations of Biomedical Language.

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the United States each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion.