Evidence map›Paper›PMID 40000174›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2025

[Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion].

Yongjian Li, Lei Liu, Meng Chen, Yixue Li, Yuchen Wang, Shoushui Wei

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2025. 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.

Yongjian LiSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
Lei LiuSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
Meng ChenSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
Yixue LiSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
Yuchen WangSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.
Shoushui WeiSchool of Control Science and Engineering, Shandong University, Jinan 250061, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.

Indexed as

Atrial FibrillationDeep LearningMachine LearningHumansRisk AssessmentAtrial fibrillation predictionFeature selectionMulti-center dataMultimodal fusion

Identifiers

PMID40000174
PMCPMC11955324

What OpenQuestion holds

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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.