ArticlePatterns (New York, N.Y.)2022
More than just sound: Harnessing metadata to improve neural network classifiers for medical auscultation.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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
2 citing papers in PubMed.
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Label-efficient algorithms are of central importance for machine learning applications in many medical fields, where obtaining expert annotations is often expensive and time-consuming. Soni et al. show how contrastive learning can help build classifiers for one of the oldest and most revered methods of clinical medicine: auscultation of heart and lung sounds.
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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.