Identifying Recurrent Non-Hodgkin Lymphoma in Structured and Unstructured Electronic Health Data

April 03, 2024 11:00 a.m. - 12:00 p.m. ET

In this webinar, University of Massachusetts Chan Medical School’s Drs. Mara Meyer Epstein and Feifan Liu will present algorithms designed to identify patients experiencing non-Hodgkin lymphoma (NHL) recurrence.

They will present the initial results of their rule-based algorithm against domain expert chart reviews, as well as describe their multi-task, multi-modal learning architecture. They’ll explain how the design makes model training data-efficient and generalizable.

Accurate cancer recurrence assessments are essential for cancer outcomes studies. Recurrence rates vary from 20-35% among survivors of common histologic subtypes of NHL; however, recurrent NHL is not reportable to cancer registries.

To address this, Drs. Epstein and Liu’s algorithms use longitudinally collected electronic health record and health claims data from two U.S. healthcare delivery systems, and one insurer, serving diverse populations.

To view upcoming speakers or recordings of past presentations, visit the CBIIT Data Science Seminar Series webpage. The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

Mara Meyer Epstein, Sc.D.

Dr. Epstein is a cancer epidemiologist with a research focus on hematological cancers, specifically multiple myeloma and its precursor, monoclonal gammopathy of undetermined significance. She focuses on utilizing electronic health records and health claims data to conduct epidemiologic research and to develop case-finding data algorithms. She is a faculty member in the Division of Health Systems Science at the University of Massachusetts Chan Medical School and at the Meyers Health Care Institute.

Feifan Liu, Ph.D.

Dr. Liu is an associate professor of the Department of Population and Quantitative Health Sciences at the University of Massachusetts Chan Medical School as well as the founding director of the innovative “AI for Health” lab. He is trained in computer science with expertise in natural language processing, machine learning, and artificial intelligence (AI) risk predictive modeling. Dr. Liu has extensive experience in exploiting advanced AI techniques to analyze heterogeneous clinical data, aiming to enhance clinical decision-making across a spectrum of critical domains including cancer treatment.

Vote below about this page’s helpfulness.

Enter the characters shown in the image.