Yilin Ning

Biostatistician, Data Scientist

Work email: yilin.ning AT duke-nus.edu.sg | Personal email: ningyilinnyl AT gmail.com


Research Fellow @ Centre for Quantitative Medicine, Duke-NUS Medical School.

Specialized in


Research interests

Explainable AI, Biostatistics, Epidemiology, Statistical programming.


2016-2020 PhD, NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore.

2010-2014 B.Sc. (Hons. 2nd upper) in Statistics, Department of Statistics and Applied Probability, National University of Singapore, Singapore.


2021 Khoo Postdoctoral Fellowship Award, Duke-NUS Medical School, Singapore, Singapore

Selected Publications

See my Google Scholar page for a complete list.

Book Chapter

Ning Y, Liu N, Ong MEH (2022). Types of Quantitative Data (Continuous, Categorical, Distributions, Skewness). Introducing, Designing and Conducting Research for Paramedics, p.115. Elsevier Health Sciences.


AI Fairness & Ethics


Ning Y#, Teixayavong S#, Shang Y, Savulescu J, Nagaraj V, Miao D, Mertens M, Ting DS, Ong JC, Liu M, Cao J, Dunn M, Vaughan R, Ong MEH, Sung JJY, Topol EJ, Liu N (2023). Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist. arXiv preprint, arXiv:2311.02107.

Liu M#, Ning Y#, Teixayavong S, Mertens M, Xu J, Ting DS, Cheng LT, Ong JC, Teo ZL, Tan TF, Narrendar RC, Wang F, Celi LA, Ong MEH, and Liu N (2023). A translational perspective towards clinical AI fairness. npj Digital Medicine, 6:172. (#: equal contribution)

Ning Y, Volovici V, Ong ME, Goldstein BA, and Liu N (2023). A roadmap to fair and trustworthy prediction model validation in healthcare. arXiv preprint, arXiv:2304.03779.

Interpretable Machine Learning: Method

Shapley Value

Ning Y, Ong MEH, Chakraborty B, Goldstein BA, Ting DSW, Vaughan R, and Liu N (2022). Shapley variable importance cloud for interpretable machine learning. Patterns 3(4):100452.

Ning Y, Li S, Ng YY, Chia MY, Gan HN, Tiah L, Mao DR, Ng WM, Leong BS, Doctor NE, Ong ME. Robust and Interpretable Machine Learning Assessment of Variable Importance with Moderate to Small Sample Sizes: A Study of Survival after Out-Of-Hospital Cardiac Arrest. Available at SSRN 4423470.

Liu M, Ning Y, Yuan H, Ong MEH, Liu N (2022). Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making. arXiv preprint, arXiv:2206.04050

Scoring System

Xie F#, Ning Y#, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, and Liu N (2023). A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols, 4(2):102302. (#: equal contribution)

Ning Y, Li S, Ong ME, Xie F, Chakraborty B, Ting DS, Liu N (2022). 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 Digit Health 1(6): e0000062.

Saffari SE#, Ning Y#, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N (2022). AutoScore-Ordinal: An Interpretable Machine Learning Framework for Generating Scoring Models for Ordinal Outcomes. BMC Medical Research Methodology 22:286. (#: equal contribution)

Xie F, Ning Y, Yuan H, Goldstein BA, Ong ME, Liu N, and Chakraborty B (2022). AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics, 125:103959.

Yuan H, Xie F, Ong MEH, Ning Y, Chee ML, Saffari SE, Abdullah HR, Goldstein BA, Chakraborty B, and Liu N (2022). AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of biomedical informatics, 129:104072.

Li S, Ning Y, Ong ME, Chakraborty B, Hong C, Xie F, Yuan H, Liu M, Buckland DM, Chen Y, and Liu N (2023). FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics, 146:104485.

Interpretable Machine Learning: Application

Shapley Value

Deng X#, Ning Y#, Saffari SE, Xiao B, Niu C, Ng SYE, Chia N, Choi X, Heng DL, Tan YJ, Ng E, Xu Z, Tay KY, Au WL, Ng A, Tan EK, Liu N, and Tan LCS (2023). Identifying clinical features and blood biomarkers associated with mild cognitive impairment in Parkinson’s Disease using machine learning. European Journal of Neurology, 00:1–9. (#: equal contribution)

Scoring System

Liu N, Liu M, Chen X, Ning Y, Lee JW, Siddiqui FJ, Saffari SE, Ho AFW, Shin SD, Ma MHM, Tanaka H, Ong MEH, PAROS Clinical Research Network Investigators (2022). Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study. eClinicalMedicine, 48:101422.

Xie F, Liu N, Yan L, Ning Y, Lim KK, Gong C, Kwan YH, Ho AF, Low LL, Chakraborty B, Ong MEH (2022). Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine, 1;45:101315.


Liu N, Wnent J, Lee JW, Ning Y, Ho AF, Siddiqui FJ, Lim SL, Chia MY, Tiah L, Mao DR, Gräsner JT (2022). Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system. Resuscitation. 176:42-50.

Biostatistics and Clinical Epidemiology


Ning Y, Lam A, Reilly M (2022). Estimating risk ratio from any standard epidemiological design by doubling the cases. BMC Medical Research Methodology, 22:157.

Chen Y#, Ning Y#, Thomas P, Salloway MK, Tan MLS, Tai ES, Kao SL, and Tan CS (2021). An open source tool to compute measures of inpatient glycemic control: translating from healthcare analytics research to clinical quality improvement, JAMIA Open, 4(2): ooab033. (#: equal contribution)

Ning Y, Ho PJ, Støer NC, Lim KK, Wee HL, Hartman M, Reilly M, and Tan CS (2021). A New Procedure to Assess When Estimates from the Cumulative Link Model Can Be Interpreted as Differences for Ordinal Scales in Quality of Life Studies. Clinical Epidemiology, 13: 53–65.

Ning Y, Tan CS, Maraki A, Ho PJ, Hodgins S, Comasco E, Nilsson KW, Wagner P, Khoo EYH, Tai ES, Kao SL, Hartman M, Reilly M, and Støer NC (2020). Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes. Statistical Methods in Medical Research, 29(2):437-454.

Ning Y, Støer NC, Ho PJ, Kao SL, Ngiam KY, Khoo EYH, Lee SC, Tai ES, Hartman M, Reilly M, and Tan CS (2020). Robust estimation of the effect of an exposure on the change in a continuous outcome. BMC Medical Research Methodology. 2020 Dec;20(1):1-1.


Li Y, Yip M, Ning Y, Chung J, Toh A, Leow C, Liu N, Ting DS, Schmetterer L, Saw SM, Jonas JB, Chia A, and Ang M (2023). Topical Atropine for Childhood Myopia Control The Atropine Treatment Long-Term Assessment Study. JAMA Ophthalmology

Liu N, Ning Y, Ong ME, Saffari SE, Ryu HH, Kajino K, Lin CH, Karim SA, Rao GR, Ho AFW, Lim SL (2022). Gender disparities among adult recipients of layperson bystander cardiopulmonary resuscitation by location of cardiac arrest in Pan-Asian communities: A registry-based study. eClinicalMedicine, 44:101293.

Chen B, Bernard JY, Padmapriya N, Ning Y, Cai S, Lança C, Tan KH, Yap F, Chong YS, Shek L, Godfrey KM, Saw SM, Chan SY, Eriksson JG, Tan CS, and Müller-Riemenschneider, F (2020). Associations between early-life screen viewing and 24 hour movement behaviours: findings from a longitudinal birth cohort study. The Lancet Child & Adolescent Health, 4(3):201-209.

Review and Perspective

Liu M, Li S, Yuan H, Ong ME, Ning Y, Xie F, Saffari SE, Shang Y, Volovici V, Chakraborty B, and Liu N (2023). Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine, 22:102587.

Li S, Liu P, Nascimento GG, Wang X, Leite FRM, Chakraborty B, Hong C, Ning Y, Xie F, Teo ZL, Ting DSW, Haddadi H, Ong MEH, Peres MA, and Liu N (2023). Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. Journal of the American Medical Informatics Association, 2023:ocad170.

Xie F, Yuan H, Ning Y, Ong ME, Feng M, Hsu W, Chakraborty B, Liu N (2022). Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of biomedical informatics, 126:103980.

Conference Presentations

2023 Robust and interpretable machine learning assessment of variable importance with moderate to small sample sizes. AMIA 2022 Annual Symposium (2023), poster presentation

2022 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. AMIA 2022 Annual Symposium (2022), oral presentation

AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes. AMIA 2022 Annual Symposium (2022), poster presentation

2021 Weighted analyses of survival outcomes under complex study designs: An R implementation. 14th International Conference of the ERCIM WG on Computational and Methodological Statistics, invited oral presentation

2019 Conditional probit model for robust inference on change in continuous outcomes. 40th Annual Conference of the International Society for Clinical Biostatistics, oral presentation

2018 Handling ties in ranks in the rank-ordered logit model. The Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018, oral presentation

Statistical Software

SamplingDesignTools. Author of the R package that implements tools for working with various epidemiological study designs for studies of binary and survival outcomes.

Research Experience

Mar-Oct 2021 Postdoctoral research assistant, Saw Swee Hock School of Public Health, National University of Singapore & Department of Medical Epidemiology and Biostatistics, Karolinska Institutet

2014-2016 Research Assistant, Yong Loo Lin School of Medicine, National University of Singapore, Singapore

Teaching Experience

2022-2023 Lecturer, GMS 5204 Data Science + Healthcare, Duke-NUS Medical School

2022 Guest Lecturer, Beyond Classic Designs and Analysis for Health Data, Karolinska Institutet & National University of Singapore

Lecturer, R for Data Science, SingHealth Academy

May 2019 Lecturer, Introduction to R Commander, Saw Swee Hock School of Public Health, National University of Singapore

Sep 2018 Tutor, StatisticAlps 2018, 8th edition: Extended use of regression models for new epidemiological designs and analyses, Bicocca Summer School, University of Milano-Bicocca