评估LLM在患者健康记录中识别罕见患者标识符的潜力
Matúš Falis1, Franz Gruber1, Samuel McInerney1
1Usher Institute, University of Edinburgh, Edinburgh.
Studies in health technology and informatics
|May 17, 2025
概括
大型语言模型可以帮助识别医疗记录中的间接患者标识符. 然而,需要进一步的研究,以提高准确性和减少虚假阳性在去识别过程中.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 医疗记录去识别 医疗记录去识别
背景情况:
- 直接的患者标识符被广泛研究为非识别.
- 医疗记录中的间接患者标识符构成重大隐私风险.
- 当前的去识别方法可能无法充分处理间接标识符.
研究的目的:
- 探索大型语言模型 (LLM) 在识别间接患者标识符方面的有效性.
- 评估LLM在增强患者记录非识别管道方面的潜力.
- 评估LLM驱动的间接风险识别的准确性和局限性.
主要方法:
- 利用大型语言模型 (LLM) 来分析患者记录的间接标识符.
- 评估了LLM识别的风险,以评估其使患者可识别的潜力.
- 评估了错误阳性率和实际可识别风险.
主要成果:
- 临床临床识别仪在自动检测间接患者识别器风险方面显示出潜力.
- 许多确定的风险是错误的阳性结果.
- 许多突出风险并不构成对患者保密的真正威胁.
结论:
- 在医疗记录中,LLM有望改善对间接患者标识符的检测.
- 需要进一步开发,以完善LLM的准确性,并尽量减少虚假阳性在去识别.
- 将LLM集成到非识别管道中的优化优化对于安全的数据访问和研究进步至关重要.
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