ArticleApplied network science2018
Predicting onset of complications from diabetes: a graph based approach.
Article in Applied network science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Pooled it
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.Journal of diabetes science and technology · 2025Article
- Exploring the lived experiences of patients with type 2 diabetes mellitus-related complications in a healthcare setting in Singapore: a phenomenological study.BMJ public health · 2025Article
- Article
- Review
- Early Detection of Diabetic Peripheral Neuropathy in Diabetic Patients: A Cross-Sectional Study.Current diabetes reviews · 2024Article
- Prediction of complications in diabetes mellitus using machine learning models with transplanted topic model features.Biomedical engineering letters · 2024Article
- Bias or biology? Importance of model interpretation in machine learning studies from electronic health records.JAMIA open · 2022Article
- Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.Diabetes spectrum : a publication of the American Diabetes Association · 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
3 authors at 2 institutions in 1 country.
Funding
Abstract
Diabetes is a significant health concern with more than 30 million Americans living with diabetes. Onset of diabetes increases the risk for various complications, including kidney disease, myocardial infractions, heart failure, stroke, retinopathy, and liver disease. In this paper, we study and predict the onset of these complications using a network-based approach by identifying fast and slow progressors. That is, given a patient's diagnosis of diabetes, we predict the likelihood of developing one or more of the possible complications, and which patients will develop complications quickly. This combination of "if a complication will be developed" with "how fast it will be developed" can aid the physician in developing better diabetes management program for a given patient.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.