Evidence map›Paper›PMID 40442041›Full record

ArticleJournal of the Chinese Medical Association : JCMA2025

Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

Yu-Wen Su, Wayne Huey-Herng Sheu, Chii-Min Hwu, Yu-Cheng Chen, Jung-Fu Chen, Yun-Shing Peng, Chien-Ning Huang, Yi-Jen Hung, Harn-Shen Chen

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In one paragraph

Article in Journal of the Chinese Medical Association : JCMA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

9 authors.

Yu-Wen SuGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ROC.
Wayne Huey-Herng SheuInstitute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli, Taiwan, ROC.
Chii-Min HwuSection of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Yu-Cheng ChenDivision of Endocrinology and Metabolism, Department of Internal Medicine, Changhua Christian Hospital Yuanlin Branch, Changhua, Taiwan, ROC.
Jung-Fu ChenDepartment of Endocrinology and Metabolism, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, ROC.
Yun-Shing PengDivision of Endocrinology and Metabolism, Department of Internal Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan, ROC.
Chien-Ning HuangInstitute of Medicine, Chung Shang Medical University Hospital, Taichung, Taiwan, ROC.
Yi-Jen HungDepartment and Graduate Institute of Biochemistry, National Defense Medical Center, Taipei, Taiwan, ROC.
Harn-Shen ChenSection of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in people with type 2 diabetes mellitus.

methodsA Patient Health Questionnaire (PHQ-9) score of 15 was selected as the cut-off for clinically meaningful depression. Missing data was either filled in with a median value, the k -nearest neighbors' method, or the entire variable was removed. Logistic regression, random forest, and decision tree machine learning models were used to decide which factors were most relevant to depression. The accuracy of each algorithm was evaluated with a testing set.

resultsWhen all variables were included in the logistic regression model, the area under the receiver operating characteristic curve was 0.81. In the random forest model, the most important factor was quality of life (QoL). Upon removing QoL-related variables, bloating, and autoimmune disease became the greatest contributing factors. Model accuracy was 83.1%. In the decision tree model, QoL was also observed as the most decisive factor. Upon removing QoL variables, bloating was the first node. Model accuracy was 82.5%.

conclusionQoL, bloating, and autoimmune disease were the most important factors associated with depression in type 2 diabetes mellitus patients.

Indexed as

AlgorithmsDepressionDiabetes Mellitus, Type 2Machine LearningAdultAgedFemaleHumansLogistic ModelsMaleMiddle AgedQuality of LifeRegistriesRisk FactorsTaiwanDepressionRisk factorTaiwanType 2 diabetes mellitus

Identifiers

PMID40442041
PMCPMC12637159

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.