Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice Study Article Swipe
Vladimir Despotović
,
Abir Elbéji
,
Kevser Fünfgeld
,
Mégane Pizzimenti
,
H. Ayadi
,
Petr V. Nazarov
,
Guy Fagherazzi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.11.23298300
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.11.23298300
Regular monitoring of respiratory quality of life (RQoL) is essential in respiratory healthcare, facilitating prompt diagnosis and tailored treatment for chronic respiratory diseases. Voice alterations resulting from respiratory conditions create unique audio signatures that can potentially be utilized for disease screening or monitoring. Analyzing data from 1908 participants from the Colive Voice study, which collects standardized voice recordings alongside comprehensive demographic, epidemiological, and patient-reported outcome data, we evaluated various strategies to estimate RQoL from voice, including handcrafted acoustic features, standard acoustic feature sets, and advanced deep audio embeddings derived from pretrained convolutional neural networks. We compared models using clinical features alone, voice features alone, and a combination of both. The multimodal model combining clinical and voice features demonstrated the best performance, achieving an accuracy of 70.34% and an area under the receiver operating characteristic curve (AUROC) of 0.77; an improvement of 5% in terms of accuracy and 7% in terms of AUROC compared to model utilizing voice features alone. Incorporating vocal biomarkers significantly enhanced the predictive capacity of clinical variables across all acoustic feature types, with a net classification improvement (NRI) of up to 0.19. Our digital voice-based biomarker is capable of accurately predicting RQoL, either as an alternative to or in conjunction with clinical measures, and could be used to facilitate rapid screening and remote monitoring of respiratory health status.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.11.11.23298300
- https://www.medrxiv.org/content/medrxiv/early/2023/11/11/2023.11.11.23298300.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388613630
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388613630Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.11.11.23298300Digital Object Identifier
- Title
-
Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice StudyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-11Full publication date if available
- Authors
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Vladimir Despotović, Abir Elbéji, Kevser Fünfgeld, Mégane Pizzimenti, H. Ayadi, Petr V. Nazarov, Guy FagherazziList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.11.11.23298300Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/11/11/2023.11.11.23298300.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/11/11/2023.11.11.23298300.full.pdfDirect OA link when available
- Concepts
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Computer science, Feature (linguistics), Speech recognition, Receiver operating characteristic, Convolutional neural network, Medicine, Artificial intelligence, Machine learning, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-11-11 |
| publication_year | 2023 |
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