用大模型增强矩阵分解，自动分离并解读临床听诊音中的异常信号。
Large Language Models and Non-Negative Matrix Factorization for Bioacoustic Signal Decomposition
- 结合大语言模型与非负矩阵分解，从重叠听诊信号中分离出可解释成分。
- 无需标注数据或先验知识，直接关联音特征与心律、呼吸等疾病。
- 适合临床辅助诊断系统开发，提升听诊分析的可解释性与智能化。
大型语言模型在解析非结构化数据方面表现出色，为超越传统数值方法解读生物医学信号提供了新路径。本研究提出一种基于大语言模型增强的矩阵分解框架，用于生物声学信号分析。重点在于分离临床录音中常见的重叠生物声学信号，通过矩阵分解将混合信号分解为可解释的成分。随后，利用大语言模型对分离后的信号进行分析，将特定声学模式与潜在病症（如心律失常或呼吸异常）建立关联。数据采集采用数字听诊器在临床模拟人上进行，确保受控且高保真环境。该混合方法无需标签数据或源类型先验知识，提供更可解释、易用的临床决策支持框架。结果表明该方法具备集成至未来智能诊断工具的潜力。
原文摘要 · Abstract (English)
Large language models have shown a remarkable ability to extract meaning from unstructured data, offering new ways to interpret biomedical signals beyond traditional numerical methods. In this study, we present a matrix factorization framework for bioacoustic signal analysis which is enhanced by large language models. The focus is on separating bioacoustic signals that commonly overlap in clinical recordings, using matrix factorization to decompose the mixture into interpretable components. A large language model is then applied to the separated signals to associate distinct acoustic patterns with potential medical conditions such as cardiac rhythm disturbances or respiratory abnormalities. Recordings were obtained from a digital stethoscope applied to a clinical manikin to ensure a controlled and high-fidelity acquisition environment. This hybrid approach does not require labeled data or prior knowledge of source types, and it provides a more interpretable and accessible framework for clinical decision support. The method demonstrates promise for integration into future intelligent diagnostic tools.
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