使用自然语言处理分析关键事件报告
Kerstin Denecke1, Helmut Paula2
1Bern University of Applied Sciences, Bern, Switzerland.
Studies in health technology and informatics
|April 29, 2024
概括
关键事件报告系统 (CIRS) 的自然语言处理 (NLP) 数据揭示了瑞士医疗保健中的关键患者安全风险. 分析发现药物错误,手术问题和患者处理是主要问题.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 患者安全 患者安全
背景情况:
- 关键事件报告系统 (CIRS) 收集员工报告可能伤害患者的事件.
- 分析这些非结构化数据对于理解和减轻医疗保健风险至关重要.
研究的目的:
- 证明NLP在从CIRS数据中提取有价值的见解方面的实用性.
- 识别瑞士医疗保健中关键事件的趋势和共同主题.
主要方法:
- 在瑞士国家CIRRNET数据库 (2006-2023) 中使用NLP分析术语,情绪和主题.
- 应用BERTopic建模来将事件报告分组为主要主题.
主要成果:
- 确定了10个主要主题,其中6个与药物错误有关.
- 主要发现包括手术错误,静脉血栓栓塞,跌倒,血液检测,COVID-19,糖尿病和儿科护理的趋势.
- 报告中很大一部分是中性 (40-50%) 或负面 (30-40%) 的.
结论:
- 对CIRS数据的NLP分析为瑞士医疗保健中关键事件的常见来源提供了宝贵的见解.
- 未来的研究将探索事件主题和情绪之间的关系.
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