在急性腹痛中用于决策支持的机器学习 - 概念证明和核心考虑
Jonas Henn1, Simon Hatterscheidt1, Anshupa Sahu2,3
1Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany.
Zentralblatt fur Chirurgie
|August 10, 2023
概括
机器学习 (ML) 模型可以预测急性腹痛患者需要紧急腹部手术的需要. 这种人工智能工具有助于决策,并优化了手术护理中的资源配置.
科学领域:
- 手术决策支持手术决策支持
- 机器学习在医学中的应用
- 紧急医疗 紧急医疗
背景情况:
- 急性腹痛是急诊室经常出现的各种原因.
- 关于紧急外科干预的决定至关重要.
- 机器学习 (ML) 为决策支持和资源优化提供了潜力.
研究的目的:
- 开发和验证ML算法来预测紧急腹部手术的必要性.
- 评估不同ML模型在分类需要手术的患者中的性能.
- 评估ML作为改善手术部门资源配置的工具.
主要方法:
- 在波恩大学医院 (2020-2021) 对1357名急性腹痛患者的回顾性分析.
- 临床参数和实验室值被用作预测因素.
- 三个ML算法 (包括基于树的方法) 在20次代中在随机分割的数据集 (80/20比) 上进行了训练和验证.
主要成果:
- 20.3%的患者需要在24小时内进行紧急手术.
- 手术治疗的患者更有可能是男性,年龄较大,存在特定的胃肠道症状,敏感性,守护性和炎症标志物升高.
- 基于树的ML算法实现了AUC ROC的中位数为0.81和0.79,证明了良好的预测性能.
结论:
- 开发了一种概念验证ML模型,用于预测急性腹部的及时手术治疗.
- 机器学习算法可以作为一种有价值的决策支持工具,特别是在资源有限的环境中.
- 将ML纳入外科培训和实践对于推进基于证据的外科手术方法至关重要.
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