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机器学习模型的开发,用于在高压氧气治疗中预测严重的突然感觉神经听力损失的预后
Hyung-Bon Koo1, SiHyeong Noh2, DongWook Lim2
1Department of Otorhinolaryngology-Head and Neck Surgery, Wonkwang University College of Medicine, Iksan, Republic of Korea.
一个机器学习模型准确地预测了严重的突然感觉神经听力损失 (SSNHL) 的恢复. 该模型包括高压氧疗法 (HBOT) 暴露,帮助SSNHL患者的个性化预后.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 严重的突然神经感官听力损失 (SSNHL) 呈现异质的原因和结果,使预后复杂化.
- 由于可变的因素,SSNHL中个体化预测恢复是具有挑战性的.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测严重SSNHL的恢复.
- 将高压氧疗法 (HBOT) 作为暴露特征用于提高预后准确性.
主要方法:
- 在2015年1月至2024年1月期间接受治疗的231名严重SSNHL患者的回顾性分析.
- 训练一个自定义的多层感知器模型,具有12个输入特征,包括人口统计,并发症,基线听力测量和HBOT变量.
- 将定制模型的性能与使用精度,F1得分,精度,回忆和AUC的传统算法进行比较.
主要成果:
- 定制的ML模型实现了89.36%的测试准确性和AUC为0.8716的AUC,优于传统方法.
- 恢复的关键预测因素包括年龄,糖尿病,头和高压氧气疗法 (HBOT) 暴露.
- 一个重要的发现是逻辑回归和SVM模型中的"≥10个HBOT会话"的预后相关性.
结论:
- 将HBOT暴露作为一个特征增加了SSNHL恢复的预测.
- 开发的ML模型可以通过提供个性化的恢复概率来帮助临床医生和患者的决策.
- 由于回顾性,单中心设计和适度的样本大小,需要进一步的外部验证.
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