Evidence map›Paper›PMID 40309130›Full record

ArticleJournal of healthcare informatics research2025

Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.

Xiao Lu, Hongli Xu, Wei Dong, Yi Xin, Jiang Zhu, Xingkang Lin, Yan Zhuang, Hebin Che, Qin Li, Kunlun He

Abstract read
In one paragraph

Article in Journal of healthcare informatics research, 2025. 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
–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. Review
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

10 authors.

Xiao Lu *Medical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.
Hongli Xu *Medical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.
Wei DongMedical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.
Yi XinDepartment of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081 China.
Jiang ZhuDepartment of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081 China.
Xingkang LinDepartment of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081 China.
Yan ZhuangMedical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.
Hebin CheMedical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.
Qin LiDepartment of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081 China.
Kunlun HeMedical Innovation Research Department of PLA General Hospital, Haidian District, No.28 Fuxing Road, Beijing, 100853 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abnormal serum sodium levels are a common and severe complication in stroke patients, significantly increasing mortality risk and prolonging ICU stays. Accurate real-time prediction of serum sodium fluctuations is crucial for optimizing clinical interventions. However, existing predictive models face limitations in handling complex dynamic features and long time series data, making them less effective in guiding individualized treatment. To address this challenge, this study developed a deep learning model based on a multi-head attention mechanism to enable real-time prediction of serum sodium concentrations and provide personalized intervention recommendations for ICU stroke patients. This study utilized publicly available MIMIC-III (

Indexed as

Dynamic prediction modelICUIrregular samplingSerum sodiumStroke

Identifiers

PMID40309130
PMCPMC12037442

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.