老年患者频繁使用紧急医疗服务:使用自然语言处理进行探索和自动识别
Maaike A Pouw1, Hidde Dijkstra2, Eduard-Raul Kőntős3
1Department of Geriatric Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Journal of the American Medical Directors Association
|August 25, 2025
概括
这项研究开发了一种自然语言处理 (NLP) 模型,以识别经常使用紧急医疗服务 (EMS) 的高风险老年人. 对于经常使用EMS的人来说,NLP模型有望改善早期识别和有针对性的干预措施.
科学领域:
- 老年学
- 卫生信息学
- 紧急医疗
背景情况:
- 人口老龄化导致医疗保健使用率增加.
- 老年人更有可能经常使用急救医疗服务 (EMS).
- 识别经常使用EMS的用户对于资源分配和干预至关重要.
研究的目的:
- 探索自然语言处理 (NLP) 方法来识别经常使用EMS的老年患者.
- 利用EMS记录中的文本数据进行风险分层.
- 为经常使用EMS的用户提供早期识别和量身定制的干预措施.
主要方法:
- 来自EMS记录的65岁及以上患者的回顾性队列研究 (2013-2019).
- 常见的EMS用户定义为每年≥3次使用.
- 在预处理的EMS文本数据 (TF-IDF矢量) 上开发了一个NLP模型.
主要成果:
- 在97,736名患者中,9. 8%是经常使用EMS的,占EMS使用率的28. 6%.
- 在训练组中,XGBoost模型的c-统计值为0.97,在测试组中为0.92.
- 模型性能指标包括回忆 (0.93训练,0.82测试) 和F1得分 (0.63训练,0.54测试),校准良好.
结论:
- 使用EMS文本数据的NLP模型可以有效地识别频繁的用户.
- 将这些NLP工具整合到医院前护理系统可以支持及时干预.
- 这种方法可能会为高使用EMS的老年人提供更为定制的护理策略.
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