Jiyoun Song

University of Pennsylvania

Jiyoun Song

Dr. Jiyoun Song, PhD, AGACNP-BC, APRN, is a faculty at the University of Pennsylvania School of Nursing. Dr. Song’s research is focused on utilizing artificial intelligence to enhance clinical decision-making. Her primary areas of interest include developing 1) tailored clinical decision support systems for individual patients to improve outcomes and reduce negative events, including rehospitalization, and 2) predictive modeling to identify at-risk patients using multidimensional approaches such as quantitative statistics and machine learning. She possesses extensive experience in managing and processing large datasets, enabling her to make more accurate predictions about patient outcomes. Her current work revolves around extracting valuable information from narrative clinical notes or verbal communication using natural language processing and speech recognition—a subset of artificial intelligence.


Countries United States;
Topics Access to care; Community-based LTC; Information and data systems in LTC; Technology and LTC;
Methods Analysis of administrative data; Data science and LTC research; Mixed methods; Quantitative data analysis;
Role Research;
Interest Groups Data Science;
GOOGLE SCHOLARhttps://scholar.google.com/citations?user=vOxSFqIAAAAJ&hl=en
Research interests

Community health, Home health care, Artificial intelligence, Data science, Clinical decision support tool, Machine learning, Natural language processing, Speech recognition

Key publications

Song, J., Min, SH., Chae, S., Bowles, K. H., McDonald, M.V., Hobensack, M., Barron, Y., Sridharan, S., Davoudi, A., Oh, S., Evans, L., & Topaz, M. (2023) Uncovering Hidden Trends: Identifying Time Trajectories in Risk Factors Documented in Clinical Notes and Predicting Hospitalizations and Emergency Department Visits during Home Health Care (Journal of the American Medical Informatics Association (JAMIA)). DOI: https://doi.org/10.1093/jamia/ocad101

Song, J., Hobensack, M., Bowles, K. H., McDonald, M.V., Cato, K., Rossetti, S., Chae, S., Kennedy E., Barron, Y., Sridharan, S., & Topaz, M. (2022) Clinical Notes: An Untapped Opportunity for Improving Risk Prediction for Hospitalization and Emergency Department Visit during Home Health Care (Journal of Biomedical Informatics). DOI: https://doi.org/10.1016/j.jbi.2022.104039

Song, J., Ojo, M., Bowles, K. H., McDonald, M.V., Cato, K., Rossetti, S., Adams, V., Chae, S., Hobensack, M., Kennedy E., Tark, A., Kang, M. J., Woo, K., Barron, Y., Sridharan, S., & Topaz, M. (2022) Detecting Language Associated with Home Healthcare Patient’s Risk for Hospitalization and Emergency Department Visit (​​Nursing Research). DOI: https://doi.org/10.1097/NNR.0000000000000586