We are a group of researchers at Duke-NUS Medical School investigating health data science, interpretable machine learning, explainable artificial intelligence, and biomedical engineering. We develop advanced computational methodologies and apply them to a wide range of clinical applications including emergency and critical care, health service research, cardiology, and many others. Our research also involves medical device innovation and technology translation.
Lab Updates
- 2023.03. Model implementation review was published in Annals of Emergency Medicine
- 2022.10. Nan was among the World’s Top 2% Scientists (Stanford University & Elsevier)
- 2022.10. MIMIC benchmark was published in Scientific Data
- 2022.09. Nature Medicine article on the proper use of AI in healthcare
- 2022.08. Nan joined the editorial board for IEEE Journal of Biomedical and Health Informatics
- 2022.06. AutoScore + ShapleyVIC scoring system was published in PLOS Digital Health
- 2022.05. P-ROSC score for out-of-hospital cardiac arrest was published in eClinicalMedicine
- 2022.04. Nan joined the editorial boards for npj Digital Medicine and PLOS Digital Health
- 2022.04. AutoScore-Imbalance was published in Journal of Biomedical Informatics
- 2022.03. SERAP triage score was published in eClinicalMedicine
- 2022.02. ShapleyVIC for interpretable machine learning was published in Patterns (Cell Press)
- 2022.02. Gender disparity in out-of-hospital cardiac arrest was published in eClinicalMedicine
- 2022.01. Review on deep learning for temporal EHR data representation was published in Journal of Biomedical Informatics
- 2022.01. AutoScore-Survival was published in Journal of Biomedical Informatics
- 2021.08. SERP triage score was published in JAMA Network Open
- 2021.06. Yilin received the Khoo Postdoctoral Fellowship Award
- 2021.06. AutoScore R package (version 0.2.0) was officially released in CRAN