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相关实验视频

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临床决策支持系统中的大型语言模型和有条件规则.

Shangeetha Sivasothy1, Adrian Bingham1, Irini Logothetis1

  • 1Applied Artificial Intelligence Initiative, Deakin University, Geelong, VIC Australia.

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概括

大型语言模型 (LLM) 和大型推理模型 (LRM) 可以生成临床决策支持系统 (CDSS) 的初始规则集,但LLM在复杂性和推理方面存在局限性. 对于CDSS规则生成,LRM提供了更高的有效性,可能减少临床医生和开发人员的时间.

关键词:
临床决策支持系统临床决策支持系统有条件规则 有条件规则大型语言模型.

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科学领域:

  • 医疗保健中的人工智能
  • 临床决策支持系统 临床决策支持系统
  • 自然语言处理自然语言处理.

背景情况:

  • 临床决策支持系统 (CDSS) 增强医疗决策,改善患者的治疗结果和医疗保健的可持续性.
  • 由于代的临床医生-开发人员反循环,制定CDSS规则是昂贵的,这会影响临床医生的患者护理可用性.

研究的目的:

  • 评估大型语言模型 (LLM) 和大型推理模型 (LRM) 在生成CDSS的分类规则集中的有效性.
  • 将人工智能生成的规则集与临床医生开发的用于COVID-19患者监测的规则集进行比较.

主要方法:

  • 使用不同的提示技术,提示各种LLM (GPT-3.5,GPT-4,GPT-4o,Gemini,克劳德3.5奏) 和LRM (GPT-o1-mini,Grok-4,克劳德4奏).
  • 评估基于准确性,可解释性和规则复杂性的规则集有效性,与流行病干预监测系统 (PiMS) CDSS进行比较.

主要成果:

  • 除非指定了PiMS变量外,LLM生成了选规则,而不是分类规则;由此产生的规则的解释性和复杂性较低,准确度从31.62%到70.71%不等.
  • 与LLMs相比,LRMs表现出不同的解释性 (3-94与PiMS' 41) 并实现了更高的准确性 (31.62%至81.70%).

结论:

  • 由于简单性和缺乏复杂的推理,LLM在模拟临床规则集方面受到限制.
  • 虽然LRM的效率有所提高,但仍然存在局限性;LLM和LRM都能提供可行的初始规则集,从而缩短了开发时间.
  • 未来的研究应该探索将LLM和LRM与决策树集成在一起,以提高CDSS规则集的有效性.