Voice EHR: Introducing Multimodal Audio Data for Health
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909381454462976 |
|---|---|
| author | Anibal, James Huth, Hannah Li, Ming Hazen, Lindsey Daoud, Veronica Ebedes, Dominique Lam, Yen Minh Nguyen, Hang Hong, Phuc Kleinman, Michael Ost, Shelley Jackson, Christopher Sprabery, Laura Elangovan, Cheran Krishnaiah, Balaji Akst, Lee Lina, Ioan Elyazar, Iqbal Ekwati, Lenny Jansen, Stefan Nduwayezu, Richard Garcia, Charisse Plum, Jeffrey Brenner, Jacqueline Song, Miranda Ricotta, Emily Clifton, David Thwaites, C. Louise Bensoussan, Yael Wood, Bradford |
| author_facet | Anibal, James Huth, Hannah Li, Ming Hazen, Lindsey Daoud, Veronica Ebedes, Dominique Lam, Yen Minh Nguyen, Hang Hong, Phuc Kleinman, Michael Ost, Shelley Jackson, Christopher Sprabery, Laura Elangovan, Cheran Krishnaiah, Balaji Akst, Lee Lina, Ioan Elyazar, Iqbal Ekwati, Lenny Jansen, Stefan Nduwayezu, Richard Garcia, Charisse Plum, Jeffrey Brenner, Jacqueline Song, Miranda Ricotta, Emily Clifton, David Thwaites, C. Louise Bensoussan, Yael Wood, Bradford |
| contents | Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Voice EHR: Introducing Multimodal Audio Data for Health Anibal, James Huth, Hannah Li, Ming Hazen, Lindsey Daoud, Veronica Ebedes, Dominique Lam, Yen Minh Nguyen, Hang Hong, Phuc Kleinman, Michael Ost, Shelley Jackson, Christopher Sprabery, Laura Elangovan, Cheran Krishnaiah, Balaji Akst, Lee Lina, Ioan Elyazar, Iqbal Ekwati, Lenny Jansen, Stefan Nduwayezu, Richard Garcia, Charisse Plum, Jeffrey Brenner, Jacqueline Song, Miranda Ricotta, Emily Clifton, David Thwaites, C. Louise Bensoussan, Yael Wood, Bradford Sound Artificial Intelligence Computers and Society Audio and Speech Processing Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI. |
| title | Voice EHR: Introducing Multimodal Audio Data for Health |
| topic | Sound Artificial Intelligence Computers and Society Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.01620 |