Voice EHR: Introducing Multimodal Audio Data for Health
<|reference_start|>Voice EHR: Introducing Multimodal Audio Data for Health: Large AI models trained on audio data may have the potential to rapidly classify patients, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets using expensive recording equipment in high-income, English-speaking countries. This challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact. 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. This application ultimately results in an audio electronic health record (voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and language with semantic meaning - compensating for the typical limitations of unimodal clinical datasets. This report introduces a consortium of partners for global work, presents the application used for data collection, and showcases the potential of informative voice EHR to advance the scalability and diversity of audio AI.<|reference_end|>
@article{anibal2024voice,
    title={Voice EHR: Introducing Multimodal Audio Data for Health},
    author={James Anibal, Hannah Huth, Ming Li, Lindsey Hazen, Veronica Daoud,
  Dominique Ebedes, Yen Minh Lam, Hang Nguyen, Phuc Hong, Michael Kleinman,
  Shelley Ost, Christopher Jackson, Laura Sprabery, Cheran Elangovan, Balaji
  Krishnaiah, Lee Akst, Ioan Lina, Iqbal Elyazar, Lenny Ekwati, Stefan Jansen,
  Richard Nduwayezu, Charisse Garcia, Jeffrey Plum, Jacqueline Brenner, Miranda
  Song, Emily Ricotta, David Clifton, C. Louise Thwaites, Yael Bensoussan,
  Bradford Wood},
    journal={arXiv preprint arXiv:2404.01620},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01620},
    primaryClass={cs.SD cs.AI cs.CY eess.AS}
}