基于睡眠的风险分层和临床结果的基础模型
Erhan Bilal1, Matheus Lima Diniz Araujo2, Kristen L Beck3
1Digital Health, IBM Research, T.J. Watson Research Center, Yorktown Heights, USA.
Research square
|April 29, 2025
概括
一个新的睡眠基础模型整合了多睡眠学 (PSG) 和电子健康记录,以识别高风险患者群体. 这种方法增强了睡眠障碍的特征,并预测了健康结果,包括死亡率.
科学领域:
- 生物医学信息学 生物医学信息学
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 睡眠障碍对发病率和死亡率有很大影响.
- 在临床实践中,多睡眠学 (PSG) 数据未得到充分利用.
- 改善睡眠功能障碍的表征可以改善患者的治疗结果.
研究的目的:
- 开发一种新的睡眠基础模型,集成PSG时间序列信号和电子医疗记录数据.
- 利用数据驱动的表示来识别具有差异性健康轨迹的患者亚群.
- 为风险分层和健康结果预测创建一个临床适用的框架.
主要方法:
- 使用基于变压器的基础模型.
- 集成的PSG时间序列信号与电子医疗记录数据.
- 分析了1万名患者的多样化数据集,平均观察期为14.5年.
- 集群模型生成的表示来识别子群.
- 在国家睡眠研究资源队列中的外部验证的发现.
主要成果:
- 确定了不同的亚群体,不同的健康轨迹.
- 风险最高的组与全因死亡率 (HR 4.83),心血管和神经结果有很强的相关性.
- 这些预测在考虑传统措施后仍然很重要.
- 外部验证证实了该模型的预测能力.
结论:
- 一个新的框架有效地利用信息密集的PSG数据来增强风险分层.
- 基础模型预测了超越传统方法的健康结果.
- 这种方法为改善睡眠医学中的患者护理提供了临床适用的工具.
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