对于可解释的生物医学信号分类的LSTM-GPT-4集成.
Kapil Kumar Reddy Poreddy1, Ajit Sahu1, Sanjoy Mukherjee1
12962 MILLBRIDGE DR, Institute of Electrical and Electronics Engineers, 2962 MILLBRIDGE DR, SANRAMON, US.
JMIR formative research
|February 7, 2026
概括
这项研究将长期短期记忆 (LSTM) 网络与GPT-4集成,以自动化生物医学信号分类和解释,改善服务不足地区的医疗保健机会. 该框架实现了高精度和有用的临床解释,为未来的部署铺平了道路.
科学领域:
- 医疗保健中的人工智能
- 生物医学信号处理
- 用于医学诊断的深度学习
背景情况:
- 数以百万计的人缺乏基本的医疗服务,诊断解释是资源有限的地区的一个关键挑战.
- 专家有限访问和复杂的信号分析 (ECG,EEG) 延迟了心血管和神经疾病的诊断.
研究的目的:
- 开发和评估一个人工智能框架,将长短期记忆 (LSTM) 网络和GPT-4结合起来,用于自动化生物医学信号分类和解释.
- 为在资源有限的环境中部署人工智能驱动的诊断工具奠定基础.
主要方法:
- 选择了双层LSTM (128→64单位) 架构来进行时间特征提取,其性能优于1D-CNN模型.
- 实施了适应模式的预处理管道和单一选.
- 该框架在公开的PhysioNet数据集 (ECG,EEG) 上进行了评估,使用患者级别的分割,并集成了GPT-4以生成临床解释.
主要成果:
- 在多个数据集中,LSTM框架实现了高分类准确性 (例如,MIT-BIH心律失常的92.3%,PTB诊断心电图的94.7%).
- 专家医生在临床准确性 (4.3/5.0),清晰性 (4.6/5.0) 和可操作性 (4.2/5.0) 方面对GPT-4生成的解释进行了高度评价,与评审者之间有很大的一致性 (κ>0.85).
结论:
- 这个概念验证证明了深度学习用于信号分类和GPT-4用于解释的可行整合.
- 开发的框架为未来的临床验证和部署在服务不足的医疗机构提供了技术基础.
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