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Liu Nan

Associate Professor

Email

Contact: 66016503

Dr Liu Nan is an Associate Professor at Centre for Quantitative Medicine and Programme in Health Services and Systems Research, Duke-NUS Medicine School. His research focuses on health services research, emergency and prehospital care, cardiology, medical informatics, and health innovation. Dr Liu has been awarded research grants from National Medical Research Council, National Health Innovation Centre, and SingHealth Foundation. He received many international and national awards, including Meritorious Paper Award from Computers in Biology and Medicine journal, Best Abstract Award from European Emergency Medical Services Congress, Grand Prize from Singapore Tech-Factor Challenge, and Paul Dudley White International Scholar Award from American Heart Association. Dr Liu is serving as an Academic Editor for four international peer-reviewed journals, including Computers in Biology and Medicine (Elsevier), Medicine (Lippincott Williams & Wilkins), PLOS ONE, and IEEE Access. He is also a regular reviewer for more than 60 international journals. Dr Liu is currently the Chairman of IEEE Engineering in Medicine and Biology Society (EMBS) Singapore Chapter.

Research Interest

 

Electronic Health Records and Medical Big Data; Physiological Signal Analysis; Deep Learning and Machine Learning; Cardiovascular Research; Prehospital and Emergency Care.

 

Research Team

 

Ning Yilin

Research Fellow

Email: yilin.ning@duke-nus.edu.sg

 

Guo Da Gang

Research Fellow

 

Lee Jin Wee

Research Fellow

Email: jw.lee@nus.edu.sg

  1. Liu N, Ong MEH, Ho AFW, Pek PP, Lu TC, Khruekarnchana P, Song KJ, Tanaka H, Naroo GY, Gan HN, Koh ZX, Ma MHM. Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients. Resuscitation 2020; 149: 53-59.
  2. Zhang Z, Zheng B, Liu N, Ge H, Hong Y. Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome. Intensive Care Medicine 2019; 45(6): 856-864.
  3. Liu N, Zhang Z, Ho AFW, Ong MEH. Artificial intelligence in emergency medicine. Journal of Emergency Critical Care Medicine 2018; 2: 82.
  4. Liu N, Sakamoto JT, Cao J, Koh ZX, Ho AFW, Lin Z, Ong MEH. Ensemble-based risk scoring with extreme learning machine for prediction of adverse cardiac events. Cognitive Computation 2017; 9(4): 545-554.
  5. Sakamoto JT, Liu N, Koh ZX, Fung NXJ, Heldeweg MLA, Ng JCJ, Ong MEH. Comparing HEART, TIMI, and GRACE scores for prediction of 30-day major adverse cardiac events in high acuity chest pain patients in the emergency department. International Journal of Cardiology 2016; 221: 759-764.
  6. Liu T, Lin Z, Ong MEH, Koh ZX, Pek PP, Yeo YK, Oh B, Ho AFW, Liu N. Manifold ranking based scoring system with its application to cardiac arrest prediction: a retrospective study in emergency department patients. Computers in Biology and Medicine 2015; 67: 74-82.
  7. Low LL, Lee KH, Ong MEH, Wang SJ, Tan SY, Thumboo J, Liu N. Predicting 30-day readmissions: performance of the LACE index compared with a regression model among general medicine patients in Singapore. BioMed Research International 2015; 2015: Article ID 169870.
  8. Liu N, Koh ZX, Chua EC, Tan LM, Lin Z, Mirza B, Ong MEH. Risk scoring for prediction of acute cardiac complications from imbalanced clinical data. IEEE Journal of Biomedical and Health Informatics 2014; 18(6): 1894-1902.
  9. Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, Ong MEH. Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Medical Informatics and Decision Making 2014; 14(1): 75.
  10. Liu N, Lin Z, Cao J, Koh ZX, Zhang T, Huang GB, Ser W, Ong MEH. An intelligent scoring system and its application to cardiac arrest prediction. IEEE Transactions on Information Technology in Biomedicine 2012; 16(6): 1324-1331.