Evidence map›Paper›PMID 42121121›Full record

Observational studyBMC medical informatics and decision making2026

Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.

Chaoshan Hu, Di Lv, Jing Liu, Lijuan Tang, Yuanmei Chen, Fang Ye, Chao Wang, Yiqiang Fan, Qi Zhang

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC medical informatics and decision making, 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
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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

9 authors.

Chaoshan Hu *School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China.
Di Lv *Department of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Jing Liu *Department of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Lijuan TangDepartment of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Yuanmei ChenDepartment of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Fang YeDepartment of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Chao WangDepartment of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
Yiqiang FanSchool of Intelligent Science and Technology, Nanjing University of Science and Technology, No. 200 Xiaoling Wei Street, Xuanwu District, Nanjing, 210094, China. yiqiang.fan50@gmail.com.
Qi ZhangDepartment of Pediatrics, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China. zhangqikeyan@163.com.

Funding

Beijing University of Chemical Technology-China -Japan Friendship Hospital Joint Fund XK2022-05National High Level Hospital Clinical Research Funding 2022-NHLHCRF-LX-01-0301
6 · The paper itself

Abstract

backgroundTraditional auscultation, heavily dependent on the subjective judgment of physicians, can lead to variability in diagnoses. This study aimed to explore the application of machine learning algorithms for analyzing breath sounds in children with asthma, particularly those with asthma in remission and those with cough variant asthma (CVA).

methodsOur study collected breath sound data from 50 children with asthma (30 with asthma in remission and 20 with CVA). First, we preprocessed and extracted the breath sound data. Second, machine learning techniques were applied to objectively classify and evaluate the breath sounds of pediatric asthma patients. Then logistic regression, random forest, and support vector machine algorithms were employed to train models and predict outcomes.

resultsIn this study, the support vector machine achieved the best performance in distinguishing between breath sounds from children with asthma in remission and those with CVA. It reached an accuracy of 98.32%, a sensitivity of 96.23%, and an area under the receiver operating characteristic curve of 0.99 in predicting pediatric asthma subtypes.

conclusionsOur findings highlight the potential of machine learning models to revolutionize the diagnosis and treatment of pediatric asthma, offering a pathway towards more precise and individualized therapeutic strategies.

trial registrationClinicalTrials.gov number: ChiCTR2300077717 (Registration Date: 2023-11-16).

Indexed as

AsthmaCough-Variant AsthmaMachine LearningRespiratory SoundsSupport Vector MachineChildChild, PreschoolClassification AlgorithmsCoughFemaleHumansMalePredictive Learning ModelsRandom ForestBreath soundsChildren with asthma in remissionCough variant asthma (CVA)Machine learning

Identifiers

PMID42121121
PMCPMC13340392

What OpenQuestion holds

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Registered trials

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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.