Biography
A master's student in the Department of Human Systems Medicine and research member in the Department of Preventive Medicine at Seoul National University, College of Medicine. Holds a bachelor's degree in Statistics and Computer Science with specialization in Computational Statistics from the University of Virginia. Passionate about applying regression models, machine learning, and quasi-experimental methods to tuberculosis and NTM research to inform policy decisions that benefit underserved communities. Committed to leveraging computational approaches to address critical challenges in disease management and improve patient outcomes.
Key Impacts
Baseline EQ-5D-5L improves prediction of treatment discontinuation in NTM pulmonary disease: Analysis from a nationwide cohort study
Despite modest overall predictive power, EQ-5D-5L scores improve the identification of NTM-PD patients at-risk for treatment discontinuation. This brief patient-reported measure requires no follow-up assessments and is less burdensome than disease-specific instruments like QOL-B. Patients identified to be at-risk for premature treatment discontinuation may require enhanced side-effect monitoring, more frequent follow-up, or adjusted treatment regimens. Future research should focus on determining clinically meaningful EQ-5D-5L thresholds and validating these findings in external cohorts to facilitate routine clinical implementation.
Source: Conference 2024