ArticleBMC endocrine disorders2025
Comparison of logistic regression and artificial neural network models for predicting hypoglycemia in non-ICU inpatients with diabetes.
Article in BMC endocrine disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimTo analyze data from non-intensive care unit (non-ICU) inpatients with diabetes to predict the risk of hypoglycemia using electronic health records (EHRs) and point-of-care (POC) blood glucose values.
methodsPatient demographics, laboratory results, POC blood glucose, and procedures were performed during the hospital stays on Days 0-2 to predict hypoglycemic episodes (blood glucose ≤ 3.9 mmol/L) on Days 3-6. The dataset was randomly split into a training set and an independent verification set at a 7:3 ratio. Logistic Regression (LR) and Artificial Neural Network (ANN) were compared using the area under the curve (AUC). A nomogram plot was also constructed to display the predicted hypoglycemia probabilities.
resultsData from 16,593 diabetic patients (January 2017 to June 2022) were analyzed. Predictive factors from the LR model included the use of insulin; previous hypoglycemia in Days 0-2; respiratory rate; blood urea nitrogen; potassium; D-dimer levels; coefficient variation of blood glucose (BG CV) > 31%; and blood glucose gap (BG gap, maximum of blood glucose - minimum of blood glucose) > 10 mmol/L. In the verification set, the AUC of ANN was 0.762 and the AUC of LR was 0.763. There was no significant difference in the effects of the models built by the two methods. The results showed that the probability predicted by the nomogram using LR is similar to the clinical results. Decision curve analysis (DCA) indicated potential clinical application for the LR model.
conclusionsThe LR model demonstrated considerable value in predicting hypoglycemia risk, comparable to ANN. Trials of such models should be conducted to evaluate their utility in reducing inpatient hypoglycemia. CLINICAL TRIAL NUMBER: Not applicable.
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.