从自由文本处方签名中提取心力衰竭的每日剂量的方法:一个比较性研究
Theodorus S Haaker1,2, Joshua S Choi1,3, Claude J Nanjo1
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.
JAMIA open
|January 6, 2025
概括
这项研究比较了从处方签名中提取每日剂量的方法. 基于正则表达式的RxSig模型是最可扩展和最具成本效益的方法,用于准确的每日剂量提取.
科学领域:
- 药物信息学 是一种信息学.
- 在医疗保健中的自然语言处理.
- 临床数据提取
背景情况:
- 从处方签名 (sigs) 中准确地提取每日剂量信息,对于药物管理和患者安全至关重要.
- 现有的自动化符号分析方法在性能,成本和可扩展性方面各不相同.
研究的目的:
- 为了比较五种不同的方法的性能,从自由文本处方签名中提取每日剂量信息.
- 确定最准确和可扩展的临床应用方法,特别是在心力衰竭和减少射出分数的背景下.
主要方法:
- 评估了五种每日剂量提取方法:Parsigs,RxSig,Sig2db,一个大语言模型 (LLM) 和一个双向长期短期记忆 (BiLSTM) 模型.
- 在29896个自由文本签名的数据集上,使用积极预测值 (PPV),灵敏度和F1得分来评估性能.
- 计算成本和完成时间也被测量.
主要成果:
- 该LLM (GPT-4o) 获得了最高的F1得分 (0.98),在灵敏度方面表现优于RxSig (0.95).
- 与LLM相比,RxSig表现出优越的正预测值 (PPV) 和显著较低的计算成本 (0美元与25美元) 和运行时间.
- 在BiLSTM模型中,F1得分是最低的 (0.71).
结论:
- 无论是LLM和RxSig模型,在从处方签名中提取每日剂量方面都显示出高准确度.
- 由于其效率和零计算成本,RxSig被确定为实际大规模实施的更可扩展和更具成本效益的方法.
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