患者安全和药物监督中的变压器类大型语言模型的双向编码器表示:对因果推断影响的全面评估
Xingqiao Wang1, Xiaowei Xu1, Zhichao Liu2
1Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
Experimental biology and medicine (Maywood, N.J.)
|December 12, 2023
概括
从变压器 (BERT) 类似模型的域特定双向编码器表示显著改善了患者安全和药物监督 (PSPV) 的因果推断. 无论模型大小或数据复杂性如何,通用BERT模型的性能都优于其他模型.
科学领域:
- 药监和患者安全 药监和患者安全
- 医疗保健中的人工智能
- 用于医疗应用的自然语言处理.
背景情况:
- 对患者安全和药监 (PSPV) 进行因果关系评估至关重要,影响安全信号检测和不良反应管理.
- 大型语言模型 (LLM),特别是来自变压器的双向编码器表示 (BERT),在PSPV应用中显示出希望.
- 在评估不同BERT类模型对增强PSPV的因果推理的适用性方面存在差距.
研究的目的:
- 探索各种类似BERT的LLM在PSPV中的因果推断的性能.
- 评估数据复杂性,模型架构和模型大小对因果推理的影响.
- 确定针对PSPV应用程序的特定领域培训和微调的有效性.
主要方法:
- 深入调查通用预先训练的BERT LLMs,特定领域的预先训练的LLMs,以及微调的特定领域的LLMs.
- 对三个公开可访问的PSPV数据集进行分析.
- 评估重点是数据复杂性,模型架构,模型大小和特定领域的调整.
主要成果:
- 与BERT类似的LLM在不同数据复杂度级别中表现出一致的预测能力.
- 预测性能和因果推理能力与类似BERT的模型大小没有直接相关.
- 具有或没有安全知识微调的领域特定预先训练的LLM在因果推断中优于通用BERT模型.
结论:
- 域特定的BERT类LLM优于PSPV中因果推理的通用模型.
- 在这种情况下,模型大小不是性能的主要决定因素.
- 这些发现为选择适合PSPV应用的LLM提供了指导.
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