Evidence map›Paper›PMID 36213916›Full record

ArticleFrontiers in psychiatry2022

Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records.

Cheng Wan, Wei Feng, Renyi Ma, Hui Ma, Junjie Wang, Ruochen Huang, Xin Zhang, Mang Jing, Hao Yang, Haoran Yu and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
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, 3 citations in OpenAlex.

  1. Article
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

11 authors at 1 institution in 1 country.

Cheng WanDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Wei FengDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Renyi MaDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Hui MaDepartment of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
Junjie WangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Ruochen HuangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Xin ZhangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Mang JingDepartment of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, China.
Hao YangDepartment of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
Haoran YuDepartment of Medical Psychology, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
Yun LiuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Nanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Diabetes and its complications are commonly associated with depressive symptoms, and few studies have investigated the diagnosis effect of depressive symptoms in patients with diabetes. The present study used a network-based approach to explore the association between depressive symptoms, which are annotated from electronic health record (EHR) notes by a deep learning model, and the diagnosis of type 2 diabetes mellitus (T2DM) and its complications. Methods: In this study, we used anonymous admission notes of 52,139 inpatients diagnosed with T2DM at the first affiliated hospital of Nanjing Medical University from 2008 to 2016 as input for a symptom annotation model named T5-depression based on transformer architecture which helps to annotate depressive symptoms from present illness. We measured the performance of the model by using the F1 score and the area under the receiver operating characteristic curve (AUROC). We constructed networks of depressive symptoms to examine the connectivity of these networks in patients diagnosed with T2DM, including those with certain complications. Results: The T5-depression model achieved the best performance with an F1-score of 91.71 and an AUROC of 96.25 compared with the benchmark models. The connectivity of depressive symptoms in patients diagnosed with T2DM ( Conclusion: The T5-depression model proposed in this study can effectively annotate depressive symptoms in EHR notes. The connectivity of annotated depressive symptoms is associated with the diagnosis of T2DM and hypertension. The changes in the network of depressive symptoms generated by the T5-depression model could be used as an indicator for screening depression.

Indexed as

depressive symptomsdiabetes complicationnatural language processingnetwork analysistype 2 diabetes mellitus

Identifiers

PMID36213916
PMCPMC9543719
OpenAlexW4296832031

What OpenQuestion holds

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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.