Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings.
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| Title: | Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings. |
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| Authors: | Low DM; Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, Massachusetts, United States of America.; McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts, United States of America., Rao V; Department of Biomedical Engineering, Columbia University, New York, New York, United States of America.; Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America., Randolph G; Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America.; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America., Song PC; Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, United States of America.; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America., Ghosh SS; Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, Massachusetts, United States of America.; McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts, United States of America.; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America. |
| Source: | PLOS digital health [PLOS Digit Health] 2024 May 30; Vol. 3 (5), pp. e0000516. Date of Electronic Publication: 2024 May 30 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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