从非结构化的临床记录中构建大规模的注册表,使用低资源的自然语言处理管道.
Nazgol Tavabi1, James Pruneski1, Shahriar Golchin2
1Department of Orthopaedic Surgery and Sports Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Artificial intelligence in medicine
|April 24, 2024
概括
一种新的方法,带有关键字的句子提取器 (SE-K),可以有效地从注册表的临床笔记中提取数据. SE-K的性能优于BERT等复杂模型,为改善患者护理和研究提供速度和可解释性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床研究 临床研究
背景情况:
- 临床注册表对于研究和患者护理质量至关重要.
- 自然语言处理 (NLP) 有助于从非结构化的临床笔记中提取信息.
- 临床注释对标准NLP模型提出了独特的挑战.
研究的目的:
- 引入带有关键词的句子提取器 (SE-K),一种高效和可解释的NLP方法.
- 将SE-K的表现与基于嵌入的NLP方法 (SE-E,BERT) 进行比较.
- 使用临床数据开发前十字带手术的全面注册表.
主要方法:
- 使用SE-K,SE-E和BERT来从临床笔记中提取信息.
- 应用方法从儿童医院的20年的非结构化临床数据.
- 使用样本外验证和AUROC指标评估的性能.
主要成果:
- 与SE-E (0.93 ± 0.04) 和BERT (0.87 ± 0.09) 相比,SE-K显示出更优异的性能 (AUROC 0.94 ± 0.04).
- 在样本以外的验证中,SE-K的表现出现了最小的下降.
- SE-K的速度至少是SE-E和BERT的六倍,提供了可解释性.
结论:
- SE-K是一种有效,高效和可解释的临床笔记分析方法.
- SE-K促进了大规模的注册表建设,质量改进和不良事件监测.
- 这种方法为临床数据提取的资源密集型NLP方法提供了有价值的替代方案.
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