Journal article
Generative AI and unstructured audio data for precision public health
- Abstract:
- In this study, transcribed videos about personal experiences with COVID-19 were used for variant classification. The o1 LLM was used to summarize the transcripts, excluding references to dates, vaccinations, testing methods, and other variables that were correlated with specific variants but unrelated to changes in the disease. This step was necessary to effectively simulate model deployment in the early days of a pandemic when subtle changes in symptomatology may be the only viable biomarkers of disease mutations. The embedded summaries were used for training a neural network to predict the variant status of the speaker as "Omicron" or "Pre-Omicron", resulting in an AUROC score of 0.823. This was compared to a neural network model trained on binary symptom data, which obtained a lower AUROC score of 0.769. Results of the study illustrated the future value of LLMs and audio data in the design of pandemic management tools for health systems.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 708.1KB, Terms of use)
-
- Publisher copy:
- 10.1038/s44401-025-00022-7
Authors
+ NIH HHS
More from this funder
- Funder identifier:
- https://ror.org/01cwqze88
- Grant:
- ZIA CL040015
- Programme:
- Intramural
+ Wellcome Trust
More from this funder
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 204904/Z/16/Z
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/W031744/1
- Publisher:
- Springer Nature
- Journal:
- npj Health Systems More from this journal
- Volume:
- 2
- Issue:
- 1
- Article number:
- 19
- Place of publication:
- England
- Publication date:
- 2025-06-02
- Acceptance date:
- 2025-04-21
- DOI:
- EISSN:
-
3005-1959
- Pmid:
-
40470007
- Language:
-
English
- Pubs id:
-
2129590
- Local pid:
-
pubs:2129590
- Deposit date:
-
2025-07-03
- ARK identifier:
Terms of use
- Copyright holder:
- Anibal et al
- Copyright date:
- 2025
- Rights statement:
- ©2025 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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