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Utility of a patient similarity-based digital tool for risk communication to patients with type 2 diabetes mellitus: perspectives from primary care physicians in ambulatory care

Ong, R., Ng, C. J., Gunasekaran, K., Liu, H., Hsu, W., Lee, M. L., & Tan, N. C. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319992 · Cited by 2 · see citing works

A Review of Machine Learning Algorithms in Diabetes Management

Gayaki, U., Daronde, S. B., Tale, A., & Barhate, A. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933462 · Cited by 2 · see citing works

Explainable Deep-Learning Model Reveals Past Cardiovascular Disease in Patients with Diabetes Using Free-Form Visit Reports

Guazzo, A., Longato, E., Fadini, G. P., Morieri, M. L., Sparacino, G., & Camillo, B. D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25599-1_32 · Cited by 2 · see citing works

A Systematic Review of Supervised Learning and Deep Learning Algorithms for Diabetic Detection

Sridevi, P., & Padmanaban, K. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933238 · Cited by 1 · see citing works

Predicting Outcomes Following Carotid Artery Stenting Using Machine Learning

Li, B., Aljabri, B. A., Beaton, D., Hussain, M. A., Lee, D. S., Wijeysundera, D. N., Rotstein, O. D., de Mestral, C., Mamdani, M., Roche‐Nagle, G., et al. (2025) · Journal of Endovascular Therapy · doi:10.1177/15266028251333670 · Cited by 1 · see citing works

Multi-Task Learning Approach for Simultaneous Prediction of Multiple Comorbidities in Diabetic Patients

Sivakumar, N. R. (2025) · International Conference on IT Innovation and Knowledge Discovery (ITIKD) · doi:10.1109/itikd63574.2025.11005343 · Cited by 1 · see citing works

Comparing the predictive performance of diabetes complications using administrative health data and clinical data

Aagaard, A., Röttger, R., Johnson, E. K., & Olsen, K. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-18079-7 · Cited by 1 · see citing works

An artificial intelligence prediction model for optimizing patient selection for cardiac imaging for the investigation of suspected coronary artery disease

Petch, J., Di, S., Watson, T., Becerra, M. P., Rosella, L. C., Natarajan, M., Sheth, T., & Schwalm, J.-D. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag040 · Cited by 1 · see citing works

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Fllatah, J., & Banjar, H. R. (2026) · JMIR Formative Research · doi:10.2196/81039 · Cited by 1 · see citing works

Identification of Patient Clusters with Distinct Disease Progression Patterns Utilizing a Nationwide Finnish Population with Type 2 Diabetes

Nygård, K., Ryhänen, R., Kortteenniemi, A., Järvinen, K., Vesikansa, A., Mehtälä, J., Koivusalo, M., Tirronen, A., Asllanaj, E., Kaukua, J., et al. (2026) · Diabetes Therapy · doi:10.1007/s13300-026-01879-z · Cited by 1 · see citing works

Comparative predictive accuracy of machine learning versus traditional statistical methods for diabetes‐related complications: A systematic review and analysis

Ghafoor, B., Zaccardi, F., Khunti, K. K., & Shabnam, S. (2026) · Diabetic Medicine · doi:10.1111/dme.70398 · Cited by 2 · see citing works

Machine Learning in Diabetes Modeling

Ardabili, S., Mosavi, A., & Felde, I. (2023) · IEEE International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy60376.2023.10417934 · Cited by 1 · see citing works

Machine Learning in Vascular Surgery: A Systematic Review and Critical AppraisalPreprint

Li, B., Feridooni, T., Cuen-Ojeda, C., Kishibe, T., de Mestral, C., Mamdani, M., & Al-Omran, M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3867678 · Cited by 1 · see citing works

Prognostic models for short-term annual risk of severe acute complications and mortality in patients living with type 2 diabetes using a national medical claim databasePreprint

Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2022) · Research Square · doi:10.21203/rs.3.rs-1604014/v1

Prognostic models for short-term annual risk of severe acute complications and mortality in patients living with type 2 diabetes using a national medical claim databasePreprint

Vimont, A., Béliard, S., Valéro, R., Leleu, H., & Durand‐Zaleski, I. (2022) · Research Square · doi:10.21203/rs.3.rs-1604014/v2

Handling Missing Values and Outliers in Advanced Data Pre-processing: An Enhancement of Diabetes Classification AccuracyPreprint

Hossain, M. S., Devnath, A., & Karmokar, P. K. (2023) · Research Square · doi:10.21203/rs.3.rs-3364064/v1

Metodologia baseada em ciência de dados para avaliação do programa de combate à dengue.

Xavier, F. W. (2023) · doi:10.11606/t.3.2023.tde-09112023-115721

Feature Minimization for Diabetic Disorders High Performances Prediction System-based on Random Forest Tree

Mohammed, S. J., Ahmed, A. M. S., & Mohammed, M. S. (2023) · JOIV International Journal on Informatics Visualization · doi:10.30630/joiv.7.3-2.1868

Análise da generalização de algoritmos de machine learning e suas aplicações na otimização de decisões em saúde

Borba, M. F. (2023) · doi:10.11606/t.6.2023.tde-05022024-163230 · Cited by 1 · see citing works

Predicting diabetes related complications in Alberta, Canada for health jurisdictions: A machine learning prediction approach

Joon, T., Li, W., Lau, D., Sharma, V., Dubois, C., Hossain, T., Dubois, T. B., Ye, M., Samanani, S., & Eurich, D. T. (2026) · Informatics and Health · doi:10.1016/j.infoh.2025.11.003

Developing Machine Learning Algorithms on Routinely Collected Administrative Health Data - Lessons from Ontario, Canada.

Harish, V., Ravaut, M., Yi, S. E., Gutierrez, J. M., Sadeghi, H., Leung, K. K., Watson, T., Kornas, K., Poutanen, T., Volkovs, M., et al. (2022) · International Journal for Population Data Science · doi:10.23889/ijpds.v7i3.1851

Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand

Nghiem, N., Wilson, N., Krebs, J. D., & Tran, T. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02678-x · Cited by 7 · see citing works

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes

Ravaut, M., Harish, V., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Watson, T., Poutanen, T., & Rosella, L. C. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.11315 · Cited by 90 · see citing works

Diabetes Mellitus Forecasting Using Population Health Data in Ontario, CanadaPreprint

Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., & Rosella, L. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04137 · Cited by 3 · see citing works