Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes

• Published in: AMIA ... Annual Symposium proceedings Vol. 2018; pp. 1056 - 1065
• Main Authors: Wang, Liqin, Lakin, Joshua, Riley, Clay, Korach, Zfania, Frain, Laura N, Zhou, Li
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2018
• Subjects: Aged; Aged, 80 and over; Data Collection; Dementia - therapy; Electronic Health Records; Female; Humans; Male; Models, Theoretical; Retrospective Studies; Terminal Care; Unsupervised Machine Learning
• ISSN: 1942-597X; 1559-4076

Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of these diseases and what care dementia patients receive in their final years of life. Using a large volume of clinical notes of dementia patients over the last two years of life, we conducted automatic topic modeling to capture the trends of various themes mentioned in care provider notes, including patients' physical function status, mental health, falls, nutrition and feeding, infections, hospital care, intensive care, end-of-life care, and family and social supports. Our research contributes to the adoption and evaluation of an unsupervised machine learning method using large amounts of retrospective free-text electronic health record data to discover and understand illness and health care trajectories.