Â鶹Éçmadou

About this webinar:

This seminar will delve into the application of natural language processing (NLP) and large language models (LLMs) in identifying people who inject drugs (PWID) within electronic health records (EHRs). It will cover the challenges of accurately extracting relevant information from unstructured text data, such as clinical notes, and discuss advanced NLP techniques for detecting key indicators of PWID. The session will highlight how LLMs can improve the identification process, facilitate data-driven healthcare interventions, and enable more effective targeting of resources for this underserved population.

About the speaker:

David Goodman-Meza, MD, PhD, is a Senior Research Associate at the Kirby Institute of the University of New South Wales (Â鶹Éçmadou). He is a physician-scientist specialised in internal medicine, infectious diseases, and addiction medicine. He received his MD with honors from Universidad Autonoma de Baja California in Tijuana, Mexico and a masters in clinical research at UCSD, and PhD at Â鶹Éçmadou. His research work is focused on the relationship of substance use disorders and infectious diseases using data science, and developing interventions to improve health outcomes in this vulnerable population. He was a recipient of a U.S. NIH/NIDA Career Development Award where he trained in the use of natural language processing and machine learning to evaluate outcomes of people who inject drugs.