Evidence mapPaperPMID 41901583Full record

ArticleMedicina (Kaunas, Lithuania)2026

Explainable Meta-Learning Ensemble Framework for Predicting Insulin Dose Adjustments in Diabetic Patients: A Comparative Machine Learning Approach with SHAP-Based Clinical Interpretability.

Emek Guldogan, Burak Yagin, Hasan Ucuzal, Abdulmohsen Algarni, Fahaid Al-Hashem, Mohammadreza Aghaei

Abstract read
In one paragraph

Article in Medicina (Kaunas, Lithuania), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Emek GuldoganDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.ORCID 0000-0002-5436-8164
Burak YaginDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.ORCID 0000-0001-6687-979X
Hasan UcuzalDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.ORCID 0000-0003-4870-3015
Abdulmohsen AlgarniDepartment of Computer Science, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0002-7556-958X
Fahaid Al-HashemDepartment of Physiology, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0001-5795-9966
Mohammadreza AghaeiDepartment of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway.ORCID 0000-0001-5735-3825

Funding

King Khalid University R.G.P.2/21/46
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Diabetes MellitusInsulinMachine LearningBoosting Machine Learning AlgorithmsHumansHypoglycemic AgentsPrediction AlgorithmsPredictive Learning ModelsHypoglycemic AgentsInsulinclinical decision supportdiabetes mellitusensemble methodsexplainable artificial intelligencegradient boostinginsulin dose predictionLIMEmachine learningmeta-learningSHAP

Identifiers

PMID41901583
PMCPMC13028235

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.