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基于机器学习的决策工具,用于选择患有异常性急性胰腺炎的患者进行内声学,以排除胆道病因
Simon Sirtl1, Michal Żorniak1,2, Eric Hohmann1
1Department of Medicine II, LMU University Hospital, Munich 81377, Germany.
World journal of gastroenterology
|September 25, 2023
概括
机器学习使用常规数据准确预测异常性急性胰腺炎 (IAP) 患者的胆汁污泥和微病. 这种工具有助于诊断IAP的原因,防止其复发,并指导进一步的诊断成像,如内镜超声波 (EUS).
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 胆汁小结石病/泥是异常性急性胰腺炎 (IAP) 的常见原因,约30%的患者发现.
- 确定IAP的病因对于预防复发性胆道胰腺炎至关重要.
研究的目的:
- 开发基于机器学习 (ML) 的决策工具.
- 该工具旨在识别患有胆汁污泥和小结石病的患者,指导使用内镜超声波 (EUS).
主要方法:
- 从218名胰腺炎患者 (2015-2020年) 的临床和实验室数据的回顾性分析.
- 被监督的ML分类人员接受了培训,以预测微石/泥.
- 该模型在两个独立的队列 (117名患者) 中得到验证.
主要成果:
- 在识别队列 (218名患者) 中,ML模型实现了0.84的准确性.
- 验证队列证实了0.76准确度的模型稳定性.
- 该模型使用了28个常规记录的患者变量.
结论:
- 一个经过验证的基于ML的预测模型可以识别胆汁污泥和微石作为AP的原因.
- 该模型使用易于获得的吸收参数.
- 这种工具有助于诊断异常性急性胰腺炎的原因.
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