开发一种预后预测模型和可视化系统,用于自主肋骨软骨鼻膜整形:一种自动机器学习方法
Aihemaitijiang Niyazi1,2, Tilimanjiang Tuohuti1, Xu Nannan3
1Department of Burns, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Frontiers in surgery
|October 20, 2025
概括
本研究引入了一种自动机器学习 (AutoML) 模型,用于预测自肋软骨鼻整形手术 (ACCR) 的结果. 该系统通过预测并发症和结果,提高了手术精度和患者满意度.
科学领域:
- 整形外科 整形外科 整形外科
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 自主肋骨软骨鼻膜整形 (ACCR) 在预测术后并发症和患者满意度方面面临挑战.
- 现有的预后模型缺乏用于个性化外科计划所需的动态风险评估和可视化能力.
- 需要先进的计算工具来改善美容手术的决策.
研究的目的:
- 开发和验证基于自动机器学习 (AutoML) 的预后预测模型,用于ACCR.
- 为实时预后和临床决策支持创建可视化系统.
- 为了解决患者满意度和术后并发症的差异.
主要方法:
- 447名ACCR患者 (2019-2024) 的回顾性分析,具有超过20个生物,外科和行为参数.
- 开发一个改进的元启发算法 (INPDOA) 用于AutoML优化,在基准函数上进行验证.
- 利用双向特征工程,用于预测重要性的SHAP值,并开发了一个基于MATLAB的临床决策支持系统 (CDSS).
主要成果:
- 在INPDOA增强的AutoML模型中,AUC为1个月的并发症达到0.867,R2为1年的鼻整形结果评估 (ROE) 得分为0.862.
- 发现的关键预测因素包括鼻腔碰撞,吸烟和手术前ROE得分.
- 决策曲线分析显示净收益有所改善;CDSS显示预测延迟减少.
结论:
- 本研究介绍了首个针对ACCR的AutoML驱动的预测框架,集成了动态风险预测和可解释的AI.
- 该框架有效地将手术精度与患者报告的结果相结合,为审美手术决策提供了一种新的方法.
- 开发的系统为提高患者护理和鼻整形术的结果提供了一个范式.
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