在急诊部分析疼痛模式:利用临床文本深度学习模型获得现实世界的洞察力
James A Hughes1, Yutong Wu2, Lee Jones3
1School of Nursing, Queensland University of Technology, Brisbane, Australia; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia.
International journal of medical informatics
|July 14, 2024
概括
超过一半的急诊室患者在抵达时经历疼痛. 一个深度学习模型确定了COVID-19大流行期间的疼痛发生率及其变化.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 疼痛是急诊室 (ED) 访问的常见原因之一.
- 准确识别疼痛患者对于及时有效治疗至关重要.
- 在ED中评估疼痛的现有方法可能不一致或劳动密集.
研究的目的:
- 确定向澳大利亚内城急诊室 (ED) 报告疼痛的患者的发生率.
- 使用临床文本深度学习算法进行自动疼痛识别.
- 分析COVID-19流行病对ED疼痛发生率和患者人口统计学的影响.
主要方法:
- 使用基于变压器的临床文本深度学习模型.
- 该模型解释了电子医疗记录中的自由文本护理评估.
- 通过使用中断时间序列分析,分析了三年来来自235,789个成年演示的数据.
主要成果:
- 在所有ED病例中,55.16%的患者在到达时报告疼痛.
- 在疼痛和没有疼痛的患者之间,在人口统计和到来/离开模式上观察到显著的差异.
- COVID-19的流行病最初减少,然后急剧增加,到达时的疼痛,改变患者人群和治疗.
结论:
- 临床文本深度学习提供了一种自动化,可重复的方法,用于从医疗记录中识别疼痛发生率.
- 这种方法为人口层面的疼痛护理和COVID-19流行病等事件的影响提供了洞察力.
- 该方法可以指导干预,并在紧急情况下评估疼痛护理质量.
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