机器学习模型用于预测耳植入后的静脉功能
Mengya Shen1,2, Xiaozhang Zhu3, Weirui Zhang3
1Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China.
Journal of otology
|November 28, 2025
概括
机器学习模型可以准确地预测感官神经听力损失 (SNHL) 患者的耳植入 (CI) 后的前庭异常. 确定的主要风险因素包括VEMP延迟,振幅和残留听力,有助于前庭康复.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 感官神经听力损失 (SNHL) 影响了许多人,经常需要耳植入 (CI).
- 静脉管功能障碍是CI后的潜在并发症,影响患者的生活质量.
- 预测CI后的前庭结局对于手术规划和康复至关重要.
研究的目的:
- 评估机器学习 (ML) 用于预测SNHL患者在CI后的前体异常.
- 开发一个实用的ML模型来预测长期前庭功能.
- 为了确定与CI后前庭功能障碍相关的显著风险因素.
主要方法:
- 在CI之前和之后收集的临床数据 (成像,VEMP,听觉信息).
- 用于数据归算和特征选的决策树算法.
- 应用六种ML方法来预测前庭功能障碍;分析特征重要性.
主要成果:
- 后勤回归在前庭功能障碍方面达到80%的准确性,在异常cVEMP方面达到78%.
- 支持矢量机 (SVM) 达到88%的准确性,用于异常的oVEMP.
- 关键预测因素包括VEMP延迟/振幅和残留听力值.
结论:
- 包括SVM和后勤回归在内的ML模型有效预测CI对前庭功能的影响.
- 这些模型为术前规划和决策提供了宝贵的见解.
- 已识别的风险因素为导向前庭康复策略提供了基础.
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