自然语言处理增加了神经外科住院患者的并发症记录
Rahul A Sastry1, Aayush Setty1,2, David D Liu1
1Department of Neurosurgery, Warren Alpert Medical School, Rhode Island Hospital, Brown University, Providence, RI, United States of America.
PloS one
|May 9, 2024
概括
使用自然语言处理的机器学习算法可以从头部成像报告中准确地识别神经外科并发症,如大脑压缩和. 这项技术有望改善文档和支持临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 神经外科 神经外科
背景情况:
- 精确识别神经外科并发症对于患者护理至关重要.
- 目前的方法可能受到复杂成像报告的手动审查的限制.
研究的目的:
- 评估一种自然语言处理 (NLP) 技术,用于识别两种常见的住院神经外科并发症.
- 评估机器学习算法对住院患者头部成像文本报告的性能.
主要方法:
- 979个大脑CT/MRI报告的数据集被用于训练和测试NLP算法.
- 随机的森林分类器被训练和测试以识别"大脑压缩"和"大脑".
主要成果:
- 随机森林算法实现了"大脑压缩"的高精度 (0.81) 和AUC (0.90).
- 对于"脑",该算法获得了极好的准确性 (0.92) 和AUC (0.94).
- 对于这两种条件,NLP算法在准确性和灵敏性方面都超过了提供商的文档.
结论:
- 基于NLP的机器学习可靠地从放射学报告中识别神经外科并发症.
- 这种方法可以作为决策支持,以增强提供者文档.
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