ArticleJournal of the American Medical Informatics Association : JAMIA2020
Predicting complications of diabetes mellitus using advanced machine learning algorithms.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Pooled it
- Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.Journal of nursing management · 2022Pooled it
- Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Diabetes and its complications: molecular mechanisms, prevention and treatment.Signal transduction and targeted therapy · 2026Review
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Predictive ability of visit-to-visit glucose variability on diabetes complications.BMC medical informatics and decision making · 2025Article
- Engineering novel features for diabetes complication prediction using synthetic electronic health records.Frontiers in genetics · 2025Article
- Can generative artificial intelligence pass the orthopaedic board examination?Journal of orthopaedics · 2024Article
- Prediction of complications in diabetes mellitus using machine learning models with transplanted topic model features.Biomedical engineering letters · 2024Article
- Group-informed attentive framework for enhanced diabetes mellitus progression prediction.Frontiers in endocrinology · 2024Article
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Article
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.Knowledge and information systems · 2023Article
- Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.Journal of medical Internet research · 2023Article
- External validation of the risk prediction model for early diabetic kidney disease in Taiwan population: a retrospective cohort study.BMJ open · 2022Article
- Bias or biology? Importance of model interpretation in machine learning studies from electronic health records.JAMIA open · 2022Article
- Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development. MATERIALS AND
methodsExperiments were conducted on the Healthcare Cost and Utilization Project State Inpatient Databases of California for the period of 2003 to 2011. Recurrent neural network (RNN) long short-term memory (LSTM) and RNN gated recurrent unit (GRU) deep learning methods were designed and compared with random forest and multilayer perceptron traditional models. Prediction accuracy of selected complications were compared on 3 settings corresponding to minimum number of hospitalizations between diabetes diagnosis and the diagnosis of complications.
resultsThe diagnosis domain was used for experiments. The best results were achieved with RNN GRU model, followed by RNN LSTM model. The prediction accuracy achieved with RNN GRU model was between 73% (myocardial infarction) and 83% (chronic ischemic heart disease), while accuracy of traditional models was between 66% - 76%. DISCUSSION: The number of hospitalizations was an important factor for the prediction accuracy. Experiments with 4 hospitalizations achieved significantly better accuracy than with 2 hospitalizations. To achieve improved accuracy deep learning models required training on at least 1000 patients and accuracy significantly dropped if training datasets contained 500 patients. The prediction accuracy of complications decreases over time period. Considering individual complications, the best accuracy was achieved on depressive disorder and chronic ischemic heart disease.
conclusionsThe RNN GRU model was the best choice for electronic medical record type of data, based on the achieved results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.