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<p>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.</p>

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Generative AI and unstructured audio data for precision public health

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

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  • James Anibal,
  • Adam Landa,
  • Hang Nguyen,
  • Veronica Daoud,
  • Tram Le,
  • Hannah Huth,
  • Miranda Song,
  • Alec Peltekian,
  • Ashley Shin,
  • Lindsey Hazen,
  • Anna Christou,
  • Jocelyne Rivera,
  • Robert Morhard,
  • Jacqueline Brenner,
  • Ulas Bagci,
  • Ming Li,
  • Yael Bensoussan,
  • David Clifton,
  • Bradford Wood

摘要

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.