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机器学习可以预测头癌的救援手术后的并发症

S Elaprolu¹, S P Sunny², L R Menon¹

¹Department of Head and Neck Surgery and Oncology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.

International journal of oral and maxillofacial surgery

|November 18, 2025

概括

这项研究开发了一种机器学习模型,用于预测头癌的救援手术后的并发症. 随机森林模型在预测潜在不良事件方面表现最好.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
手术瘤学手术瘤学

背景情况:

头癌的救生手术具有严重的并发症风险.
准确预测这些并发症对于患者的管理和结果至关重要.

研究的目的:

开发和评估机器学习模型,用于预测头癌救援手术后的并发症.
确定救援后手术发病率的关键预测因素.

主要方法:

对277名头癌患者进行救援手术的回顾性分析.
机器学习模型的比较:逻辑回归,规则化的逻辑回归,随机森林和支持向量机 (SVM).
确定并发症的重要预测特征.

主要成果:

在277名患者中,125人经历了并发症.
随机森林模型在预测并发症方面获得了最高的灵敏度 (80.0%) 和特异性 (71.0%).
关键预测因素包括重建状态,无病间隔,手术类型,酒精史和复发节点阳性.

结论:

机器学习,特别是随机森林算法,在预测头癌救援手术后的并发症方面是有效的.
鉴定的特征为风险分层和个性化患者护理提供了宝贵的见解.

关键词:

头癌是头部和部的癌症.机器学习是机器学习.疾病发病率疾病发病率随机森林算法随机森林算法复发情况复发情况

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