预测慢性病患者的风险因素和心电图变化
Sanjaya Kumar Panigrahi1, Madhuchhanda Pattnaik2, Aruna Acharya3
1Department of Physiology, 627109 MKCG Medical College and Hospital, Berhampur, Ganjam, 760004, India.
Journal of basic and clinical physiology and pharmacology
|February 3, 2026
概括
这项研究使用生物传感器数据和机器学习开发了慢性病 (CKD) 患者心血管事件的准确预测模型. 早期发现风险有助于预防CKD的进展和并发症.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
背景情况:
- 慢性病 (CKD) 是一个重大的全球健康挑战,增加了患病率和死亡率,特别是从心血管事件.
- 有效的CKD管理需要早期识别和干预风险因素,以防止疾病进展和相关并发症.
- 在不同的人群中CKD的异质性使得通用预测策略的开发变得复杂.
研究的目的:
- 在被诊断为慢性病的患者中开发心血管事件的准确预测模型.
- 整合生物传感器捕获的生理参数与机器学习技术,以增强预测能力.
- 探索遗传和非遗传风险评分与心血管风险评估中的生理学数据结合在一起的有用性.
主要方法:
- 利用生物传感器捕获关键的生理参数:氧和度 (SpO2),脉冲率 (PR),输液指数 (Pi),呼吸速率 (RRp) 和Pleth变化指数 (PVi).
- 应用堆叠自动编码器 (SAE) 用于对被捕获的生理数据进行诊断分析.
- 开发了基因风险评分 (GRS) 和非基因风险评分 (NGRS) 模型,用于风险因素评估,使用几率比 (OR) 的自然对数.
主要成果:
- 开发了整合各种因素加权贡献的预测模型,以预测CKD患者的心血管事件.
- 为了提高模型的准确性,实施了一种包含自动机器学习 (AutoML) 的新型机器学习方法.
- 综合模型表明,在CKD患者群体中改善心血管风险预测的潜力.
结论:
- 开发的模型有效地整合了生理性质和风险因素,为预测慢性脏病中心血管事件做出了加权贡献.
- 自动机器学习 (AutoML) 的整合代表了CKD并发症预测建模的重大进步.
- 这些发现支持使用先进的机器学习和全面的生理监测来积极管理慢性病患者的心血管风险.
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