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使用可穿戴传感器数据进行有效和可解释的疲劳预测的选择性RAG增强混合ML-LLM框架.

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  • 1Department of Medical Informatics, College of Medicine, Korea University, Seoul 02841, Republic of Korea.

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概括
此摘要是机器生成的。

一个新的混合机器学习和大型语言模型框架提高了高压力工作的疲劳分类准确性. 这种方法提高了使用可穿戴传感器数据的预测可靠性和解释性.

关键词:
可以解释的人工智能AI疲劳的预测和疲劳的预测混合推理推理是混合推理.大型语言模型 (LLM)机器学习是机器学习.可以穿戴的传感器.

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科学领域:

  • 计算科学是一种计算科学.
  • 职业健康 职业健康 职业健康 职业健康
  • 人工智能的人工智能是人工智能.

背景情况:

  • 疲劳严重影响要求高的职业的表现.
  • 可穿戴式传感器提供持续监控,但传统的机器学习 (ML) 模型在现实世界中缺乏可靠性和可解释性.
  • 现有的疲劳预测模型与不稳定,校准不佳和不透明的输出作斗争.

研究的目的:

  • 为高压力职业开发一个可靠和可解释的疲劳分类框架.
  • 将ML的效率与大型语言模型 (LLM) 的推理能力相结合,以提高预测.
  • 通过可穿戴数据提高疲劳监测的可靠性和透明度.

主要方法:

  • 开发了一个选择性取回增强生成 (RAG) 增强的混合ML-LLM框架.
  • ML模型 (逻辑回归,XGBoost,LSTM) 在297名紧急响应人员的可穿戴和生态瞬间评估数据上进行了训练.
  • 对于边界ML预测 (0.45 ≤ p ≤ 0.55) 的LLM被选择性地激活,使用符号规则和检索的例子.

主要成果:

  • 混合框架在不确定性区域改善了分类性能 (准确度:0.556到0.617,精度:0.684到0.703,回忆:0.635到0.748,F1:0.659到0.725).
  • 总体测试组的性能也得到了改善 (准确度:0.707到0.718,精度:0.739到0.741,回忆:0.918到0.937,F1:0.819到0.827).
  • SHAP和LLM分析确定了睡眠时间和心率变化作为关键预测因素,提高了模型的透明度.

结论:

  • 通过RAG增强的混合ML-LLM框架显著提高了疲劳分类的稳定性,可解释性和效率.
  • 这种可扩展的解决方案为高压力职业中的真实世界疲劳监测提供了更可靠的方法.
  • ML和LLM的整合为疲劳预测提供了透明的解释,解决了传统模型的局限性.