Jiahong Zhang

Jiahong Zhang’s research interests lie at the intersection of population health, spatial science, healthcare systems, and explainable artificial intelligence. His work focuses on developing interpretable machine learning frameworks that examine how clinical, behavioral, and environmental factors shape health disparities and population outcomes.

During his M.S. in Analytics at the University of Southern California, Jiahong conducted multiple healthcare-focused studies using large-scale electronic health record (EHR) databases, including MIMIC-III and MIMIC-IV. His research explored predictive modeling for in-hospital mortality, stroke risk, and hospital readmission, with a particular emphasis on model interpretability, feature selection, and clinically meaningful decision support systems. Through collaborations with the Los Angeles General Medical Center (LAC + USC), he also studied healthcare operations and appointment optimization, developing simulation-based scheduling models to improve clinic utilization and reduce patient wait times.

His growing interest in spatial determinants of health led him to research infrastructure accessibility and regional disparities using geospatial clustering and machine learning methods. These experiences strengthened his interest in integrating EHR data, neighborhood-level socioeconomic indicators, and environmental exposures to better understand population health inequities across place and time.

At USC, Jiahong hopes to develop interdisciplinary research that bridges population health, behavioral science, and spatial analytics to support more equitable and explainable public health interventions. He is particularly interested in integrating electronic health records, environmental exposures, and neighborhood-level socioeconomic factors to better understand how place-based conditions contribute to health disparities and healthcare accessibility in underserved communities.

M.S. in Analytics, University of Southern California

B.S. in Joint Mathematics-Economics, University of California, San Diego

Zhang, J., Li, H., Ashrafi, N., Yu, Z., Placencia, G., Pishgar, M. Prediction of In-Hospital Mortality for ICU Patients with Heart Failure. medRxiv. 2024 Jun 25. doi: 10.1101/2024.06.25.24309448. medRxiv Preprint

Ashrafi, N., Abdollahi, A., Zhang, J., Pishgar, M. Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database. arXiv. 2024 Sep 3. arXiv:2409.01685 [cs.LG]. arXiv:2409.01685

Zhang, J., et al. Prediction of In-Hospital Mortality for ICU Patients with Heart Failure. Presented at the CIE51 International Conference on Industrial Engineering, Sydney, Australia, 2024.