一种人工神经网络方法来诊断和预测肝脏功能障碍和肝脏衰竭在重症监护环境中
S Pappada1,2,3, B Sathelly3, J Schmieder4
1Department of Anesthesiology, College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio, USA.
Hippokratia
|October 14, 2024
概括
机器学习模型可以预测ICU中的严重肝功能障碍,从而能够更早地进行干预. 这种人工智能工具提供肝衰竭风险指数,以便及时诊断和改善患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的机器学习
- 肝功能分析 肝功能分析
背景情况:
- 在重症监护室 (ICU) 检测肝功能障碍是具有挑战性的,因为它往往是无症状的.
- 在重症监护机构中,延迟诊断肝功能衰竭导致患者的治疗结果不佳.
- 早期发现肝功能障碍对于有效干预至关重要.
研究的目的:
- 开发一种预测模型,用于在ICU中早期诊断严重的肝功能障碍/肝衰竭.
- 创建基于机器学习的指数,用于评估重症监护患者的肝衰竭概率.
- 为了提高肝功能障碍的诊断准确性和及时性.
主要方法:
- 利用全面的开放访问患者数据库进行模型开发和验证.
- 开发并验证了两个人工神经网络模型,以创建肝衰竭风险指数 (0-100).
- 纳入临床生命体征和肝功能测试实验室结果用于模型培训.
主要成果:
- 性能最好的模型实现了83.3%的灵敏度和77.5%的特异性,用于诊断严重的肝功能障碍.
- 该模型预测了临床诊断前17.5小时的中位数严重的肝功能障碍.
- 证明在重症监护机构中诊断的重要时间.
结论:
- 机器学习有助于在重症患者中早期及时干预肝功能障碍.
- 开发的模型有助于医疗保健提供者预防或最大限度地减少与肝衰竭相关的发病率和死亡率.
- 这种预测方法对于优化重症监护室患者护理至关重要.
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