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预测机器学习模型用于预测在伊朗班达尔阿巴斯进行整体手术后的术后并发症:研究协议

Majid Vatankhah Tarbebar¹, Milad Mohammadi¹, Vahid Mehrnoush²

¹Department of Anesthesiology, Critical Care and Pain Management Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran (the Islamic Republic of).

|October 28, 2025

概括

机器学习模型将预测一般手术患者的术后并发症. 这项研究旨在确定风险因素,以改善外科护理质量和患者的治疗结果.

科学领域:

改善外科手术的质量.
医疗信息学医学信息学
在医疗保健中的预测分析.

背景情况:

尽量减少术后并发症对于提高手术护理质量至关重要.
了解术后并发症的发生和风险因素是必不可少的.
机器学习 (ML) 为开发外科手术中的预测模型提供了潜力.

研究的目的:

开发一般手术后术后并发症的风险因素预测模型.
帮助外科医生识别高风险患者.
通过早期风险识别来改善患者的治疗结果.

主要方法:

在第三级推中心对一般外科患者 (年龄≥18) 的前性分析.
从2025年9月到2026年9月收集数据,不包括不完整的记录.
应用四种监督的ML技术:后勤回归,决策树,随机森林和极端梯度提升,以预测30天的术后并发症.

主要成果:

模型将使用准确性,精度,回忆和F1评分进行评估.
确定影响并发症风险的关键患者相关,手术相关和术后因素.
对不同ML算法的性能进行比较,以预测并发症.

结论:

ML模型可以有效地预测一般外科手术中的术后并发症.
识别风险因素可以指导有针对性的干预措施,以减少并发症.
这种方法有可能提高外科护理质量和患者安全.

关键词:

机器学习机器学习风险因素风险因素在外科手术方面.

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