Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Random Forests

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Random survival forests

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Generalized random forests

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RANDOM SURVIVAL FORESTS

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Survival as a function of HbA1c in people with type 2 diabetes: a retrospective cohort study

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Long-term Effects of Metformin on Metabolism and Microvascular and Macrovascular Disease in Patients With Type 2 Diabetes Mellitus

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High-Dimensional Variable Selection for Survival Data

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Mortality and cardiovascular risk associated with different insulin secretagogues compared with metformin in type 2 diabetes, with or without a previous myocardial infarction: a nationwide study

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Variable importance in binary regression trees and forests

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Evaluation of different biological data and computational classification methods for use in protein interaction prediction

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Variable importance in binary regression trees and forests

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Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

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Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study

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Visit-to-Visit Low-Density Lipoprotein Cholesterol Variability and Risk of Cardiovascular Outcomes

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Sulfonylureas and the Risks of Cardiovascular Events and Death: A Methodological Meta-Regression Analysis of the Observational Studies

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Impact of Glycemic Variability on Chromatin Remodeling, Oxidative Stress, and Endothelial Dysfunction in Patients With Type 2 Diabetes and With Target HbA1c Levels

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Identifying Important Risk Factors for Survival in Patient With Systolic Heart Failure Using Random Survival Forests

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Visit-to-Visit Glycemic Variability and Risks of Cardiovascular Events and All-Cause Mortality: The ALLHAT Study

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Reactive Oxygen Species, Endoplasmic Reticulum Stress and Mitochondrial Dysfunction: The Link with Cardiac Arrhythmogenesis

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Glucose variability, HbA1c and microvascular complications

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Acute and chronic fluctuations in blood glucose levels can increase oxidative stress in type 2 diabetes mellitus

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Reverse Epidemiology of Traditional Cardiovascular Risk Factors in the Geriatric Population

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Prenatal antidepressant use and risk of attention-deficit/hyperactivity disorder in offspring: population based cohort study

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Total cholesterol and all-cause mortality by sex and age: a prospective cohort study among 12.8 million adults

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The HbA1c and All-Cause Mortality Relationship in Patients with Type 2 Diabetes is J-Shaped: A Meta-Analysis of Observational Studies

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A Machine Learning Aided Systematic Review and Meta-Analysis of the Relative Risk of Atrial Fibrillation in Patients With Diabetes Mellitus

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Predictions of diabetes complications and mortality using hba1c variability: a 10-year observational cohort study

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Beyond HbA1c: comparing glycemic variability and glycemic indices in predicting hypoglycemia in type 1 and type 2 diabetes

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Prognostic Significance of Long-term HbA1c Variability for All-Cause Mortality in the ACCORD Trial

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Pioglitazone, a peroxisome proliferator-activated receptor-gamma activator, attenuates atrial fibrosis and atrial fibrillation promotion in rabbits with congestive heart failure

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Short‐term prediction of mortality in patients with systemic lupus erythematosus: Classification of outcomes using random forests

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Variability in HbA1c , blood pressure, lipid parameters and serum uric acid, and risk of development of chronic kidney disease in type 2 diabetes

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Thiazolidinedione use and atrial fibrillation in diabetic patients: a meta-analysis

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Visit-to-visit cholesterol variability correlates with coronary atheroma progression and clinical outcomes

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Molecular and electrophysiological mechanisms underlying cardiac arrhythmogenesis in diabetes mellitus

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Comparative cardiovascular risk in users versus non-users of xanthine oxidase inhibitors and febuxostat versus allopurinol users

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PPAR‐γ Activator Pioglitazone Prevents Age‐Related Atrial Fibrillation Susceptibility by Improving Antioxidant Capacity and Reducing Apoptosis in a Rat Model

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Sex-Based Differences in Outcomes of Oral Anticoagulation in Patients With Atrial Fibrillation

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Low Lipid Levels and High Variability are Associated With the Risk of New‐Onset Atrial Fibrillation

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High-Intensity Interval Training in Patients with Type 2 Diabetes Mellitus: a Systematic Review

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Pioglitazone attenuates atrial remodeling and vulnerability to atrial fibrillation in alloxan‐induced diabetic rabbits

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Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome

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Depressive Symptoms, Co-Morbidities, and Glycemic Control in Hong Kong Chinese Elderly Patients With Type 2 Diabetes Mellitus

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Person-Centered, Outcomes-Driven Treatment: A New Paradigm for Type 2 Diabetes in Primary Care

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Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels

CHANDRAN, S. R., Vigersky, R. A., Thomas, A., Lim, L., Ratnasingam, J., Tan, A. T. B., & Gardner, D. S.-L. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0277 · Cited by 14 · see citing works

Variability of metabolic parameters and risk of heart failure: Can it be a marker of incident heart failure?

Roever, L., Tse, G., & Biondi‐Zoccai, G. (2019) · International Journal of Cardiology · doi:10.1016/j.ijcard.2019.07.007 · Cited by 5 · see citing works

Comparative effects of sodium glucose cotransporter 2 (SGLT2) inhibitors and dipeptidyl peptidase-4 (DPP4) inhibitors on new-onset atrial fibrillation and stroke outcomesPreprint

Lee, S., Zhou, J., Chang, C., Liu, T., Chang, D., Wong, W. T., Leung, K. S. K., Wai, A. K. C., Cheung, B. M. Y., Tse, G., et al. (2021) · medRxiv · doi:10.1101/2021.01.04.21249211 · Cited by 4 · see citing works

Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine LearningPreprint

Zhou, J., Wong, K. H. G., Lee, S., Liu, T., Leung, K. S. K., Jeevaratnam, K., Cheung, B. M. Y., Wong, I. C. K., Zhang, Q., & Tse, G. (2021) · medRxiv · doi:10.1101/2021.01.16.21249934 · Cited by 2 · see citing works

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh, E., Sharifdini, A., Ghazzaghi, H., & Ghobadi, M. Z. (2022) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-022-00969-9 · Cited by 134 · see citing works

Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitors vs. Dipeptidyl Peptidase-4 (DPP4) Inhibitors for New-Onset Dementia: A Propensity Score-Matched Population-Based Study With Competing Risk Analysis

Mui, J. V., Zhou, J., Lee, S., Leung, K. S. K., Lee, T. T. L., Chou, O. H. I., Tsang, S. L., Wai, A. K. C., Liu, T., Wong, W. T., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.747620 · Cited by 114 · see citing works

The Effect of Berberine on Metabolic Profiles in Type 2 Diabetic Patients: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials

Guo, J., Chen, H., Zhang, X., Lou, W., Zhang, P., Qiu, Y., Zhang, C., Wang, Y., & Liu, W. J. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/2074610 · Cited by 110 · see citing works

Pioglitazone Inhibits Diabetes-Induced Atrial Mitochondrial Oxidative Stress and Improves Mitochondrial Biogenesis, Dynamics, and Function Through the PPAR-γ/PGC-1α Signaling Pathway

Zhang, Z., Zhang, X., Meng, L., Gong, M., Li, J., Shi, W., Qiu, J., Yang, Y., Zhao, J., Suo, Y., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.658362 · Cited by 79 · see citing works

Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification

Tse, G., Lee, Q., Chou, O. H. I., Chung, C. T., Lee, S., Chan, J. S. K., Li, G., Kaur, N., Roever, L., Liu, H., et al. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102168 · Cited by 47 · see citing works

Lower risk of gout in sodium glucose cotransporter 2 (SGLT2) inhibitors versus dipeptidyl peptidase-4 (DPP4) inhibitors in type-2 diabetes

Zhou, J., Liu, X., Chou, O. H. I., Li, L., Lee, S., Wong, W. T., Zhang, Q., Chang, C., Liu, T., Tse, G., et al. (2022) · Lara D. Veeken · doi:10.1093/rheumatology/keac509 · Cited by 38 · see citing works

Metformin versus sulphonylureas for new onset atrial fibrillation and stroke in type 2 diabetes mellitus: a population-based study

Zhou, J., Zhang, G., Chang, C., Chou, O. H. I., Lee, S., Leung, K. S. K., Wong, W. T., Liu, T., Wai, A. K. C., Cheng, S. H., et al. (2022) · Acta Diabetologica · doi:10.1007/s00592-021-01841-4 · Cited by 36 · see citing works

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

Lee, S., Zhou, J., Leung, K. S. K., Wu, W. K. K., Wong, W. T., Liu, T., Wong, I. C. K., Jeevaratnam, K., Zhang, Q., & Tse, G. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001950 · Cited by 36 · see citing works

Risk stratification of cardiac arrhythmias and sudden cardiac death in type 2 diabetes mellitus patients receiving insulin therapy: A population‐based cohort study

Lee, S., Jeevaratnam, K., Liu, T., Chang, D., Chang, C., Wong, W. T., Wong, I. C. K., Lip, G. Y. H., & Tse, G. (2021) · Clinical Cardiology · doi:10.1002/clc.23728 · Cited by 30 · see citing works

HbA1c Variability and the Risk of Dementia in Patients with Diabetes: A Meta‐Analysis

Song, J., Bai, H., Xu, H., Xing, Y., & Chen, S. (2022) · International Journal of Clinical Practice · doi:10.1155/2022/7706330 · Cited by 27 · see citing works

NADPH Oxidase Mediates Oxidative Stress and Ventricular Remodeling through SIRT3/FOXO3a Pathway in Diabetic Mice

Qiu, J., Liu, D., Li, P., Zhou, L., Zhou, L., Liu, X., Zhang, Y., Yuan, M., Tse, G., Li, G., et al. (2022) · Antioxidants · doi:10.3390/antiox11091745 · Cited by 26 · see citing works

High visit-to-visit cholesterol variability predicts heart failure and adverse cardiovascular events: a population-based cohort study

Chan, J. S. K., Satti, D. I., Lee, Y. H. A., Hui, J. M. H., Lee, T. T. L., Chou, O. H. I., Wai, A. K. C., Ciobanu, A., Liu, Y., Liu, T., et al. (2022) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwac097 · Cited by 25 · see citing works

Identifying Disease and Diagnosis in Females Using Machine Learning

Pramanik, S., & Bandyopadhyay, S. K. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch187 · Cited by 25 · see citing works

Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature

Gosak, L., Martinović, K., Lorber, M., & Stiglic, G. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13894 · Cited by 24 · see citing works

Lipid variability and risk of microvascular complications in patients with diabetes: a systematic review and meta-analysis

Karimi, M. A., Vaezi, A., Ansari, A., Archin, I., Dadgar, K., Rasouli, A., Ghannadikhosh, P., Alishiri, G., Tizro, N., Gharei, F., et al. (2024) · BMC Endocrine Disorders · doi:10.1186/s12902-023-01526-9 · Cited by 24 · see citing works

Synergistic Anti-inflammatory and Neuroprotective Effects of Cinnamomum cassia and Zingiber officinale Alleviate Diabetes-Induced Hippocampal Changes in Male Albino Rats: Structural and Molecular Evidence

Almohaimeed, H. M., Mohammedsaleh, Z. M., Batawi, A. H., Balgoon, M. J., Ramadan, O., Baz, H. A., Jaouni, S. K. A., & Ayuob, N. N. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.727049 · Cited by 24 · see citing works

Risk of New-Onset Prostate Cancer for Metformin Versus Sulfonylurea Use in Type 2 Diabetes Mellitus: A Propensity Score–Matched Study

Lee, Y. H. A., Zhou, J., Hui, J. M. H., Liu, X., Lee, T. T. L., Hui, K., Chan, J. S. K., Wai, A. K. C., Wong, W. T., Liu, T., et al. (2022) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2022.7010 · Cited by 25 · see citing works

Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes

Fu, X., Wang, Y., Cates, R. S., Li, N., Liu, J., Ke, D., Liu, J., Liu, H., & Yan, S. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1061507 · Cited by 24 · see citing works

Hard Voting Ensemble Approach for the Detection of Type 2 Diabetes in Mexican Population with Non-Glucose Related Features

Morgan-Benita, J. A., Galvan-Tejada, C. E., Cruz, M., Galván-Tejada, J. I., Gamboa-Rosales, H., Arceo-Olague, J. G., Luna-García, H., & Celaya-Padilla, J. M. (2022) · Healthcare · doi:10.3390/healthcare10081362 · Cited by 21 · see citing works

A Pragmatic Comparison of Supervised Machine Learning Classifiers for Disease Diagnosis

Altaf, I., Butt, M. A., & Zaman, M. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544582 · Cited by 20 · see citing works

Type 2 cytokines promote angiogenesis in ischemic muscle via endothelial IL-4Rα signaling

Li, H., He, C., Zhu, R., Chen, F. M., Wang, L., Leung, F. P., Tian, X. Y., Tse, G., & Wong, W. T. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112964 · Cited by 19 · see citing works

Lipid variability and risk of microvascular complications in Action to Control Cardiovascular Risk in Diabetes ( ACCORD ) trial: A post hoc analysis

Hukportie, D. N., Li, F., Zhou, R., Zheng, J., Wu, X., Zou, M., & Wu, X. (2022) · Journal of Diabetes · doi:10.1111/1753-0407.13273 · Cited by 16 · see citing works

Association Between Visit-to-Visit Lipid Variability and Incident Cancer: A Population-based Cohort Study

Chan, J. S. K., Satti, D. I., Lee, Y. H. A., Waleed, K. B., Tang, P., Mahalwar, G., Minhas, A. M. K., Roever, L., Biondi‐Zoccai, G., Leung, F. P., et al. (2022) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2022.101421 · Cited by 18 · see citing works

High Triglyceride Variability Increases the Risk of First Attack of Acute Pancreatitis

Tung, Y., Hsiao, F., Lin, C., Ho, C.-T., Hsu, T., Chiang, H., & Chu, P. (2023) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000002198 · Cited by 18 · see citing works

HbA1c variability associated with dementia risk in people with type 2 diabetes

Moran, C., Whitmer, R. A., Dove, Z., LACY, M. E., Soh, Y., Tsai, A., Quesenberry, C. P., Karter, A. J., Adams, A. S., & Gilsanz, P. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14066 · Cited by 18 · see citing works

Dysglycemia and arrhythmias

Sun, D.-K., Zhang, N., Liu, Y., Qiu, J., Tse, G., Li, G., Roever, L., & Liu, T. (2023) · World Journal of Diabetes · doi:10.4239/wjd.v14.i8.1163 · Cited by 17 · see citing works

Glycemic variability and the risk of atrial fibrillation: a meta-analysis

Li, W., Wang, Y., & Zhong, G.-Q. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1126581 · Cited by 16 · see citing works

Systemic Immune-Inflammation Index and Related Hematologic Markers as Prognostic Tools in Type 2 Diabetes

Cosma-Lăzuran, R., Leucuta, D., & Popoviciu, M. S. (2025) · Medicina · doi:10.3390/medicina61081433 · Cited by 15 · see citing works

Predictive value of neutrophil‐to‐lymphocyte ratio for atrial fibrillation and stroke in type 2 diabetes mellitus: The Hong Kong Diabetes Study

Chang, C., Zhou, J., Chou, O. H. I., Chan, J., Leung, K. S. K., Lee, T. T. L., Wong, W. T., Wai, A. K. C., Liu, T., Zhang, Q., et al. (2022) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.397 · Cited by 15 · see citing works

Predicting 1-year mortality of patients with diabetes mellitus in Kazakhstan based on administrative health data using machine learning

Alimbayev, A., Zhakhina, G., Gusmanov, A., Sakko, Y., Yerdessov, S., Arupzhanov, I., Kashkynbayev, A., Zollanvari, A., & Gaipov, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-35551-4 · Cited by 14 · see citing works

Gender- and Age-Specific Associations of Visit-to-Visit Blood Pressure Variability With Anxiety

Zhou, J., Lee, S., Wong, W. T., Leung, K. S. K., Nam, R. H. K., Leung, P., Chau, Y.-L. A., Liu, T., Chang, C., Cheung, B. M. Y., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.650852 · Cited by 15 · see citing works

Development of an Electronic Frailty Index for Predicting Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement using Machine Learning

Chen, Y., Zhou, J., Chan, J. S. K., Liu, T., Hothi, S. S., Roever, L., Rajan, R., Wong, I. C. K., Zhang, Q., Tse, G., et al. (2022) · Annals of Clinical Cardiology · doi:10.4103/accj.accj_13_22 · Cited by 13 · see citing works

The relationship of low-density lipoprotein cholesterol and all-cause or cardiovascular mortality in patients with type 2 diabetes: a retrospective study

Chang, C.-H., Yeh, S., Ooi, S.-W., Li, C., & Chen, H.-F. (2023) · PeerJ · doi:10.7717/peerj.14609 · Cited by 12 · see citing works

The association between blood pressure variability and hip or vertebral fracture risk: A population-based study

Zhou, J., Li, H., Chang, C., Wu, W. K. K., Wang, X., Liu, T., Cheung, B. M. Y., Zhang, Q., Lee, S., & Tse, G. (2021) · Bone · doi:10.1016/j.bone.2021.116015 · Cited by 12 · see citing works

The Neutrophil-to-Lymphocyte Ratio Predicts Cardiovascular Outcomes in Patients With Diabetes: A Systematic Review and Meta-Analysis

Dabaghi, G. G., Rad, M. R., Mortaheb, M., Darouei, B., Amani‐Beni, R., Mazaheri‐Tehrani, S., Izadan, M., & Touhidi, A. (2024) · Cardiology in Review · doi:10.1097/crd.0000000000000820 · Cited by 12 · see citing works

HbA1c Variability and Cardiovascular Events in Patients with Prostate Cancer Receiving Androgen Deprivation Therapy

Chan, J. S. K., Lee, Y. H. A., Liu, K., Hui, J. M. H., Dee, E. C., Ng, K., Satti, D. I., Liu, T., Tse, G., & Ng, C. (2022) · European Urology Open Science · doi:10.1016/j.euros.2022.11.002 · Cited by 11 · see citing works

Visit-to-Visit Variability in Fasting Blood Glucose Predicts the New-Onset Heart Failure: Results From Two Large Chinese Cohorts

Wang, Y., Zhou, J., Qi, W., Zhang, N., Tse, G., Li, G., Wu, S., & Liu, T. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.101842 · Cited by 10 · see citing works

Development of a population pharmacokinetic model of pyrazinamide to guide personalized therapy: impacts of geriatric and diabetes mellitus on clearance

Kim, R., Jayanti, R. P., Lee, H., Kim, H. K., Kang, J., Park, I.-N., Kim, J., Oh, J. Y., Kim, H. W., Lee, H., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1116226 · Cited by 10 · see citing works

Changes over Time in Hemoglobin A1C (HbA1C) Levels Predict Long-Term Survival Following Acute Myocardial Infarction among Patients with Diabetes Mellitus

Plakht, Y., Gilutz, H., & Shiyovich, A. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10153232 · Cited by 9 · see citing works

Diagnostic Performance of Machine Learning Algorithms for Predicting Heart Failure in Diabetic Patients: A Systematic Review and Meta‐Analysis

Eini, P., Eini, P., Serpoush, H., & Rezayee, M. (2025) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70111 · Cited by 8 · see citing works

High-Intensity Statin vs. Low-Density Lipoprotein Cholesterol Target for Patients Undergoing Percutaneous Coronary Intervention: Insights From a Territory-Wide Cohort Study in Hong Kong

Ng, A. K., Ng, P. Y., Ip, A., & Siu, C. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.760926 · Cited by 8 · see citing works

Association of Visit‐to‐Visit Variability in Fasting Plasma Glucose with Digestive Cancer Risk

Zhang, N., Wang, Y., Tse, G., Li, G., Wu, S., & Liu, T. (2022) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2022/4530894 · Cited by 8 · see citing works

The association between variability of risk factors and complications in type 2 diabetes mellitus: a retrospective study

Chen, M., Pu, L., Gan, Y., Wang, X., Kong, L., Guo, M., Yang, H., Li, Z., & Xiong, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-56777-w · Cited by 8 · see citing works

Uncovering the Lipid Web: Discovering the Multifaceted Roles of Lipids in Human Diseases and Therapeutic Opportunities

Pandey, M. K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241713223 · Cited by 7 · see citing works

Clinical application of machine learning and Internet of Things in comorbid depression among diabetic patients

Liu, H., Zhang, W., Goh, C., Dai, F., Sadiq, S., & Tse, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00024-1 · Cited by 7 · see citing works

Risk Factors of Pancreatic Cancer in Patients With Type 2 Diabetes Mellitus: The Hong Kong Diabetes Study

Chan, R. N. C., Lee, T. T. L., Chou, O. H. I., So, J., Chung, C. T., Dee, E. C., Ng, K., Tang, P., Roever, L., Liu, T., et al. (2022) · Journal of the Endocrine Society · doi:10.1210/jendso/bvac138 · Cited by 7 · see citing works

Association Between Atherosclerosis-Related Cardiovascular Disease and Uveitis: A Systematic Review and Meta-Analysis

Gao, X., Lv, T., Li, G., Tse, G., & Liu, T. (2022) · Diagnostics · doi:10.3390/diagnostics12123178 · Cited by 7 · see citing works

Impact of baseline blood pressure on all-cause mortality in patients with atrial fibrillation: results from a multicenter registry study

Xu, W., Song, Q., Zhang, H., Wang, J., Shao, X., Wu, S., Zhu, J., Cai, J., & Yang, Y. (2023) · Chinese Medical Journal · doi:10.1097/cm9.0000000000002627 · Cited by 7 · see citing works

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

Pucchio, A., Krance, S. H., Pur, D. R., Bhatti, J., Bassi, A., Manichavagan, K., Brahmbhatt, S., Aggarwal, I., Singh, P., Virani, A., et al. (2023) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-023-06100-6 · Cited by 7 · see citing works

PowerAI-CVD – the first Chinese-specific, validated artificial intelligence-powered in-silico predictive model for cardiovascular diseasePreprint

Li, L., Chou, O. H. I., Lü, L., Pui, H. H. H., Lee, Q., Kaur, N., Wong, W. T., Chang, C., Liu, H., Wai, A. K. C., et al. (2023) · medRxiv · doi:10.1101/2023.10.08.23296722 · Cited by 7 · see citing works

Visit-to-visit lipid variability, coronary artery calcification, inflammation, and mortality in the Multi-Ethnic Study of Atherosclerosis

Chan, J. S. K., Satti, D. I., Chan, R. N. C., Chevli, P. A., Mehta, A., Martin, S. S., Sharma, G. V., Tse, G., Virani, S. S., & Shapiro, M. D. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaf085 · Cited by 6 · see citing works

A scoping review of digital solutions in diabetes outpatient care: Functionalities and outcomes

Wang, W., Samadbeik, M., Puri, G., McLeod, D. S. A., Lobo, E. H., Quang, T. D., Nguyen, J., Ding, M., & Sullivan, C. M. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105967 · Cited by 7 · see citing works

Establishment and external validation of an early warning model of diabetic peripheral neuropathy based on random forest and logistic regression

Wang, L., Li, J., Lin, Y., Yuan, H., Fang, Z., Fei, A., Shen, G.-M., & Jiang, A. (2024) · BMC Endocrine Disorders · doi:10.1186/s12902-024-01728-9 · Cited by 5 · see citing works

Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus

Vershinina, O., Sabbatinelli, J., Bonfigli, A. R., Colombaretti, D., Giuliani, A., Krivonosov, M., Trukhanov, A., Franceschi, C., Ivanchenko, M., & Olivieri, F. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1689312 · Cited by 4 · see citing works

Seasonal fluctuations of CGM metrics in individuals with type 1 diabetes using an intermittently scanned CGM device or sensor augmented pump

Oi-Yo, Y., Urai, S., Yamamoto, A., Takayoshi, T., Yamamoto, M., Hirota, Y., & Ogawa, W. (2024) · Endocrine · doi:10.1007/s12020-024-03971-5 · Cited by 4 · see citing works

WoS Driven Bibliometric Analysis of Diabetes Mellitus Prediction Using Machine Learning and Deep Learning

Kaur, I., & Ali, A. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070435 · Cited by 4 · see citing works

Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review

Ghosh, K., Chandra, S., Ghosh, S., & Ghosh, U. S. (2025) · Cureus · doi:10.7759/cureus.91520 · Cited by 4 · see citing works

An up-to-date systematic review on machine learning approaches for predicting treatment response in diabetes

Wu, W., Zhang, W., Sadiq, S., Tse, G., Khalid, S. G., Fan, Y., & Liu, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00027-7 · Cited by 4 · see citing works

High visit-to-visit cholesterol variability predicts new-onset heart failure and adverse cardiovascular events: a retrospective population-based cohort study

Chan, J. S. K., Satti, D. I., Lee, Y. H. A., Hui, J. M. H., Lee, T. T. L., Chou, O. H. I., Wai, A. K. C., Ciobanu, A., Liu, T., Liu, T., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehab849.153 · Cited by 4 · see citing works

The Impact of Cardiac Comorbidity Sequence at Baseline and Mortality Risk in Type 2 Diabetes Mellitus: A Retrospective Population-Based Cohort Study

Lee, S., Huang, H. Y. R., Lee, T. T. L., Chung, C. T., Chou, O. H. I., Leung, K. S. K., Wai, A. K. C., Wong, W. T., Liu, T., Chang, C., et al. (2022) · Life · doi:10.3390/life12121956 · Cited by 4 · see citing works

Association between stress hyperglycemia ratio and neovascular glaucoma in patients with proliferative diabetic retinopathy

Fang, C., He, D., Shen, M., Chen, R., & Shen, X. (2025) · BMC Ophthalmology · doi:10.1186/s12886-025-03982-4 · Cited by 3 · see citing works

A Domain Oriented Framework for Prediction of Diabetes Disease and Classification of Diet Using Machine Learning Techniques

Bhat, S. S., & Ansari, G. A. (2023) · Advanced technologies and societal change · doi:10.1007/978-981-99-0377-1_13 · Cited by 3 · see citing works

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Gan, Y., Chen, M., Kong, L., Wu, J., Pu, Y. D., Wang, X., Zhou, J., Fan, X., Xiong, Z., & Qi, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1216897 · Cited by 3 · see citing works

Role of angiotensin receptor-neprilysin inhibitor in diabetic complications

Liu, Y., Lu, C.-Y., Zheng, Y., Zhang, Y., Qian, L.-L., Li, K.-L., Tse, G., Wang, R.-X., & Liu, T. (2024) · World Journal of Diabetes · doi:10.4239/wjd.v15.i5.867 · Cited by 3 · see citing works

Predictions of arrhythmic, heart failure and mortality outcomes in pericarditis using automatic electrocardiogram analysisPreprint

Lakhani, I., Zhou, J., Wong, M. V., Lee, S., Lau, J. K. Y., Shi, S., Wong, W. T., Liu, T., Zhang, Q., & Tse, G. (2021) · medRxiv · doi:10.1101/2021.11.07.21266025 · Cited by 3 · see citing works

The incidence rate of allergic reactions induced by oxaliplatin is higher in patients with rectal cancer compared with colon cancer

Liu, T., Jin, Y., Yang, X., Tong, Z., & Dong, M. (2023) · Drug and Chemical Toxicology · doi:10.1080/01480545.2023.2217700 · Cited by 2 · see citing works

Lipid Variability and the Risk of Incident Peripheral Artery Disease

Tung, Y., Tsai, T.-H., Hsieh, Y.-J., Hsu, T., Hsiao, F., Lin, C., & Chu, P. (2025) · Circulation Journal · doi:10.1253/circj.cj-25-0504 · Cited by 2 · see citing works

Higher metabolic variability increases the risk of depressive disorder in type 2 diabetes mellitus: a longitudinal nationwide cohort study

An, J. H., Han, K., & Jeon, H. J. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1217104 · Cited by 2 · see citing works

Risk factors of dementia in type 2 diabetes mellitus: The Hong Kong diabetes study

Chau, Y.-L. A., Yoo, J. W., Zhou, J., LiutaoGuo, C., Wong, W. T., Chang, C., Liu, T., Jeevaratnam, K., Zhang, Q., Tse, G., et al. (2023) · Aging and Health Research · doi:10.1016/j.ahr.2023.100155 · Cited by 2 · see citing works

PowerAI-Diabetes: Review of glycemic and lipid variability to predict cardiovascular events in Chinese diabetic population

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An Optimized Deep Learning-based framework for predicting diabetes mellitus using FFNN

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Lipid Swings Provoke Vascular Inflammation

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