ArticleCyborg and bionic systems (Washington, D.C.)2024
Federated Abnormal Heart Sound Detection with Weak to No Labels.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Radiomics-guided multimodal MRI fusion framework for non-enhanced glioblastoma noninvasive identification.PLOS digital health · 2026Article
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on cardiotocogram data.Scientific reports · 2025Article
- Effectiveness of machine learning models in diagnosis of heart disease: a comparative study.Scientific reports · 2025Article
- Enhanced anomaly network intrusion detection using an improved snow ablation optimizer with dimensionality reduction and hybrid deep learning model.Scientific reports · 2025Article
- ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.BMC cardiovascular disorders · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
- Advances in Biointegrated Wearable and Implantable Optoelectronic Devices for Cardiac Healthcare.Cyborg and bionic systems (Washington, D.C.) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases are a prominent cause of mortality, emphasizing the need for early prevention and diagnosis. Utilizing artificial intelligence (AI) models, heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions. However, real-world medical data are dispersed across medical institutions, forming "data islands" due to data sharing limitations for security reasons. To this end, federated learning (FL) has been extensively employed in the medical field, which can effectively model across multiple institutions. Additionally, conventional supervised classification methods require fully labeled data classes, e.g., binary classification requires labeling of positive and negative samples. Nevertheless, the process of labeling healthcare data is time-consuming and labor-intensive, leading to the possibility of mislabeling negative samples. In this study, we validate an FL framework with a naive positive-unlabeled (
Identifiers
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Registered trials
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.