CogProg:利用大型语言模型进行即时健康评估预测
Gina Sprint1, Maureen Schmitter-Edgecombe2, Raven Weaver2
1Gonzaga University, Spokane, WA USA.
概括
大型语言模型 (LLM) 在预测自我报告的健康状况方面表现有前途. 当提供文本描述时,LLM在预测心理敏度,疲劳和压力水平方面提高了准确性.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 计算心理学 计算心理学
背景情况:
- 预测未来的健康状况有助于了解健康模式,并积极支持认知和身体挑战.
- 生成型大语言模型 (LLM) 正在成为各种预测任务的有效工具,包括涉及非结构化数据和可解释推理的预测任务.
研究的目的:
- 研究大语言模型 (LLM) 在准确预测未来自我报告的健康状况方面的有效性.
- 评估LLM与健康状况预测中的传统数值方法的性能.
主要方法:
- 利用来自多项研究的心理敏度,疲劳和压力的即时评估.
- 使用每天的反应 (N=106) 和活动的文字描述 (N=32) 构建的提示/响应对来预测随后的自我报告健康.
- 微调了几个LLM,并应用了思维链,促使评估预测准确性和可解释性.
主要成果:
- 在LLM中,总体平均绝对误差 (MAE) 达到0.851.1,这是最低的.
- 随着额外的文本上下文,多式联络LLM显示了心理敏度 (0.862),疲劳 (1000) 和压力 (0.414) 的最低MAE.
- 多模式LLM在压力预测RMSE中表现优于数值基线 (0.947),而传统算法在心理敏性和疲劳方面表现优越.
结论:
- 简单的健康预测方法 (LLM),特别是当加上基于文本的上下文信息时,可以有效地提高健康预测的准确性.
- 这项研究为LLM在预测性健康监测和个性化干预方面的潜在应用提供了宝贵的见解.
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