A/Prof Liu Nan is an Associate Professor at the Centre for Quantitative Medicine (CQM) and Programme in Health Services and Systems Research (HSSR), Duke-NUS Medical School. He is also a faculty member at NUS Institute of Data Science, and a graduate faculty member at Duke University, USA. Clinically, Dr Liu is affiliated with SingHealth.
A/Prof Liu is actively working on AI, machine learning, and data science with their applications in various clinical domains. He is also interested in technology translation and commercialization. He co-founded TIIM Healthcare Pte Ltd and serves as its Scientific Advisor. His research has been funded by the National Medical Research Council (NMRC), National Research Foundation (NRF), National Health Innovation Centre (NHIC), Ministry of Education (MOE), AI Singapore, Duke-NUS Medical School, and SingHealth Foundation, as well as industrial partners such as Continental AG.
A/Prof Liu serves and has served as Associate Editor/Editorial Board Member for more than 10 international peer-reviewed journals, including npj Digital Medicine, IEEE Journal of Biomedical and Health Informatics, and PLOS Digital Health. Additionally, he is a regular reviewer for more than 80 international journals, including The Lancet and Nature Medicine. He also serves on the Program Committees of a number of premium AI and data science conferences such as AAAI, NeurIPS, and AMIA.
Visit his website at Digital Medicine Lab.
Interpretable and Trustworthy Machine Learning
Explainable Artificial Intelligence
Deep Learning and Health Data Science
Electronic Health Records
Medical Image Analysis
Physiological Signal Analysis
Prehospital and Emergency Care
Adjunct Senior Research Fellow
Lee Jin Wee
Senior Research Assistant
Yeung Kar Fu
Senior Research Assistant
1. Volovici V, Syn NL, Ercole A, Zhao JJ, Liu N. Steps to avoid overuse and misuse of machine learning in clinical research. Nature Medicine 2022 Oct; 28(10): 1996-1999.
2. Xie F, Zhou J, Lee JW, Tan M, Li SQ, Rajnthern L, Chee ML, Chakraborty B, Wong AKI, Dagan A, Ong MEH, Gao F, Liu N. Benchmarking emergency department triage prediction models with machine learning and large public electronic health records. Scientific Data 2022 Oct; 9: 658.
3. Ning Y, Li S, Ong MEH, Xie F, Chakraborty B, Ting DSW, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022 Jun; 1(6): e0000062.
4. Liu N, Liu M, Chen X, Ning Y, Lee JW, Siddiqui FJ, Saffari SE, Matthew M, Shin SD, Tanaka, Ho AFW, Ong MEH. Development and validation of interpretable prehospital return of spontaneous circulation (P-ROSC) score for out-of-hospital cardiac arrest patients using machine learning. eClinicalMedicine 2022 Jun; 48: 101422.
5. Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, Liu N. AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics 2022 May; 129: 104072.
6. Ning Y, Ong MEH, Chakraborty B, Goldstein BA, Ting DSW, Vaughan R, Liu N. Shapley variable importance cloud for interpretable machine learning. Patterns 2022 Apr; 3: 100452.
7. Xie F, Yuan H, Ning Y, Ong MEH, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics 2022 Feb; 126: 103980.
8. Xie F, Ong MEH, Liew JNMH, Tan KBK, Ho AFW, Nadarajan GD, Low LL, Kwan YH, Goldstein BA, Matchar DB, Chakraborty B, Liu N. Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Network Open 2021 Aug; 4(8): e2118467.
9. Liu N, Guo DG, Koh ZX, Ho AFW, Xie F, Tagami T, Sakamoto JT, Pek PP, Chakraborty B, Lim SH, Tan JWC, Ong MEH. Heart rate n-variability (HRnV) with its application to risk stratification of chest pain patients in the emergency department. BMC Cardiovascular Disorders 2020; 20: 168.
10. Xie F, Chakraborty B, Ong MEH, Goldstein B, Liu N. AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics 2020; 8(10): e21798.