معرفی
Chin Siang Ong is an Assistant Professor of Surgery (Surgical Outcomes) at Yale University School of Medicine. He holds an MBBS from National University of Singapore (2010), PhD from Johns Hopkins University (2019), and MPH from Harvard University (2025). His research focuses on integrating machine learning with surgical outcomes, particularly in cardiac surgery and critical care. Key areas include developing AI-driven decision support systems, predicting postoperative complications, and optimizing healthcare workflows. He collaborates on projects such as SurgeryLLM, a large language model framework for surgical decision-making, and studies clinical variables influencing readmissions and mortality. His work addresses challenges in ECMO management, fetal surgery simulation, and healthcare policy impacts on TAVR utilization.
Education: MBBS (2010, NUS), Residency (General Surgery 2011, Cardiothoracic Surgery 2015 at National University Health System), Research Fellowships at Johns Hopkins Hospital and Massachusetts General Hospital (2020–2021).
Research Interests: Machine learning applications in surgery, surgical outcomes analysis, cardiovascular device development, and translational clinical informatics. His lab explores causal inference methods, predictive modeling for readmissions, and biomaterials for vascular grafts.
Publications span machine learning in appendicitis diagnosis, cardiac surgery bleeding management, and macrophage roles in atrial fibrillation. He contributes to editorial work on AI in physiology and collaborates with institutions like the Stephen & Denise Adams Center for Parkinson’s Disease Research at Yale.
