机器学习模型的应用和解释,用于预测成年人严重阻塞性睡眠呼吸暂停的风险
Yewen Shi1, Yitong Zhang1, Zine Cao1
1Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China.
BMC medical informatics and decision making
|October 20, 2023
概括
机器学习模型可以预测严重的阻塞性睡眠呼吸暂停 (OSA) 风险. 梯度增强机模型确定了关键因素,如腰围和睡眠分数,用于早期诊断和治疗.
科学领域:
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
- 机器学习 机器学习
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种广泛存在的疾病,具有复杂的诊断.
- 严重的OSA与多个身体系统的功能障碍有关.
- 准确的风险预测对于及时干预至关重要.
研究的目的:
- 开发一个可解释的机器学习 (ML) 模型来预测严重的OSA风险.
- 确定导致严重OSA的关键临床风险因素.
- 创建一个可访问的风险评估工具.
主要方法:
- 对1656名接受过多睡眠图 (PSG) 的患者进行了回顾性分析.
- 使用AUC对六种分类模型 (LR,GBM,XGBoost,AdaBoost,Bagging,MLP) 的评估.
- 应用夏普利添加式解释 (SHAP) 模型解释性和风险因素分析.
主要成果:
- 61.47%的受试者患有严重的OSA.
- 梯度增强机 (GBM) 模型实现了最高的性能 (AUC=0.857).
- 关键预测指标包括腰围,子周长,爱普沃思睡眠度量表,年龄和柏林问卷.
结论:
- 开发并验证了用于严重OSA风险预测的ML模型.
- GBM 模型提供了卓越的预测性能和可解释性.
- 在线计算器有助于早期识别和管理严重的OSA.
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