A/Prof Liu Nan - Department of Biomedical Informatics

LIU NAN

Division of Biomedical Informatics

Associate Professor

Personal Website

Brief Introduction Teaching Areas/Graduate Supervision Journals & Publications

Brief Introduction

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 leads the Duke-NUS AI + Medical Sciences Initiative (DAISI). 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 has served as an Editor for more than 10 prestigious peer-reviewed journals, including npj Digital Medicine, PLOS Medicine, BMC Medicine, and IEEE Journal of Biomedical and Health Informatics. Additionally, he is a regular reviewer for many international journals such as The Lancet, JAMA 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.

Qualifications:

Ph.D. (Machine Learning)

Awards

World’s Top 2% Scientists, Stanford University & Elsevier
Visionary Industry Technology Award, International Federation for Emergency Medicine
Secretariat Award, American Academy of Ophthalmology

Teaching Areas/Graduate Supervision

Journals & Publications

Google Scholar: https://scholar.google.com.sg/citations?user=ceF698kAAAAJ&hl=en

• Liu M, Ning Y, Teixayavong S, Mertens M, Xu J, Ting DSW, Cheng LTE, Ong JCL, Teo ZL, Tan TF, RaviChandran N, Wang F, Celi LA, Ong MEH, Liu N. A translational perspective towards clinical AI fairness. npj Digital Medicine 2023 Sep; 6: 172.
• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

Personal Website