Title: Scalable Natural Language Processing for Transforming Medicine

Speaker: Monica Agrawal

Abstract:
The data in electronic health records (EHRs) have immense potential to transform medicine both at the point-of-care and through retrospective research. However, structured data alone can only tell a fraction of patients' clinical narratives, as many clinically important variables are trapped within clinical notes. In this talk, I will discuss scalable natural language processing (NLP) solutions to overcome these technical challenges in clinical information extraction. These include the development of label-efficient modeling methodology, novel techniques for leveraging large language models, and a new paradigm for EHR documentation that incentivizes the creation of high-quality data at the point-of-care.

Speaker Bio:
Dr. Monica Agrawal is an incoming assistant professor at Duke University, joint between the Department of Biostatistics and Bioinformatics and the Department of Computer Science, as well as the co-founder of a new health technology startup. In her research, she tackles diverse challenges including scalable clinical information extraction, smarter electronic health records, and human-in-the-loop systems. Her work has been published at venues in machine learning, natural language processing, computational health, and human-computer interaction. She recently earned her PhD in Computer Science in the Clinical Machine Learning Group at MIT and previously obtained a BS/MS in Computer Science from Stanford University.

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