Using Machine Learning to Delineate Patterns in Patient Journeys

Abstract: 

Slowing the progression to psoriatic arthritis among patients with psoriasis is crucial to prevent irreversible joint damages. Besides several risk factors, little is known about the complex patterns of psoriasis progression. We will present how we used longitudinal electronic health records and machine learning to uncover hidden patterns in psoriasis patient journey.

Bio: 

Jenna Eun is a principal data scientist in Janssen, a pharmaceutical company of Johnson & Johnson. She is part of the Commercial Data Sciences, Data Engineering & Data Enablement Leadership Team. In her role, she leads a portfolio of business-critical projects, including novel applications of machine learning to drive improved patient adherence, retention, and clinical outcome across key products and regions. Jenna holds a bachelor’s degree and a Ph.D. in Biochemistry from the University of Wisconsin-Madison where she conducted research in biophysics, chemical biology, and biomedical engineering. Prior to joining Janssen, she was selected as Helen Hay Whitney Howard Hughes Medical Institute postdoctoral fellow at Harvard University.

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