Unlike structured data, unstructured data are often buried within free text clinical narratives that are difficult to analyze and interpret to derive useful insights. Free text cannot be easily categorized in the same way that a structured, numerical data point can, and unstructured data often have nuances that are not easily captured or represented in structured data.

This session will cover methods and techniques for interpreting and converting unstructured text into useful research data using two related, but distinct approaches: (1) Natural Language Processing (NLP), a specialized branch of AI focused on the interpretation and manipulation of human-generated spoken or written data; and (2) information retrieval, which often underlies many search engine technologies. This session will also highlight EMERSE, an open-source information retrieval tool that has been designed to help everyday users work with the free text documents (i.e., clinical notes) in medical records that is now being adopted by other academic medical centers.

Finally, attendees will hear directly from researchers about how they have used these methods and tools to enhance their research by accessing and harnessing the power of unstructured data.

Speakers:
David A. Hanauer, MD, MS, FACMI, FAMIA
Director of MICHR Informatics Program
Associate Professor of Learning Health Sciences

BRINGING DATA TO THE PEOPLE: HOW A SECURE, SELF-SERVICE, FREE-TEXT SEARCH TOOL CAN EMPOWER CLINICAL RESEARCH TEAMS AND IMPROVE PRODUCTIVITY

VG Vinod Vydiswaran, PhD
Associate Professor of Learning Health Sciences
Associate Professor School of Information

PROMISE OF UNSTRUCTURED DATA

Discussants:
Christina Angeles, MD
Assistant Professor, Surgery (General Surgery)
Assistant Professor, Dermatology

Xu Shi, Ph.D.
CCMB Affiliate Faculty
Assistant Professor of Biostatistics, School of Public Health

Leslie Yuan, MPH
Chief Information Officer, Clinical and Translational Science
(CTSI) , University of California San Francisco