一个新的自然语言处理工具改善了结肠镜检查腺瘤和有的息肉检测率
Melissa Chew1, Catherine Yu1, Leanne Stojevski2
1Department of Gastroenterology, Northern Health, Melbourne, Victoria, Australia.
一个新的自然语言处理 (NLP) 代码准确地从结肠镜报告中确定腺瘤检测率 (ADR) 和状息肉检测率 (SDR). 这种自动化方法显著提高了结肠镜质量审计的效率和准确性.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 结肠镜检查和组织学报告的手动匹配对于确定腺瘤检测率 (ADR) 和状息肉检测率 (SDR) 是耗时的.
- 需要自动化数据提取方法来提高结肠镜质量审计的效率.
研究的目的:
- 验证自然语言处理 (NLP) 代码,以快速高效地提取数据来计算ADR和SDR.
- 在第三级卫生服务环境中评估NLP代码的准确性和时间效率.
主要方法:
- 开发了一个NLP代码,从结肠镜检查和组织学报告中提取质量指标.
- 使用手动审查来验证NLP代码提取的ADR和SDR的一致性.
- 该NLP代码经历了代改进和验证在单独的队列.
主要成果:
- 在初始培训阶段,NLP代码实现了99.9%的患者名称一致性和98.9%的ADR和SDR准确性.
- 经过代码修改后,准确性在培训队列中提高到100%,在验证队列中提高到99.9%.
- 使用NLP的验证阶段的审计时间减少到不到一个小时.
结论:
- 一个自动化的NLP代码可以准确地确定ADR和SDR,在第三级结肠镜服务中准确度接近100%.
- 广泛采用NLP为准确和时间效率高的结肠镜检查提供了显著的改进.
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