Generative AI and Unstructured Audio Data for Precision Public Health.

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

npj health systems（2025）

Oxford University Clinical Research Unit | Morsani College of Medicine | College of Engineering | National Library of Medicine | Feinberg School of Medicine

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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.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
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【要点】：本研究使用转录的关于COVID-19个人经历的视频进行变种分类，证明了生成式AI和音频数据在精确公共卫生领域的潜在价值。

【方法】：研究利用o1语言模型对视频转录进行总结，并去除与特定变种相关但不涉及疾病变化的变量，以此训练神经网络预测发言者的变种状态。

【实验】：实验使用了关于COVID-19个人经历的转录视频，通过训练的神经网络模型，以"Omicron"或"Pre-Omicron"为预测目标，获得了0.823的AUROC分数，相较于基于二元症状数据的模型（AUROC分数为0.769）表现更佳。