开发和验证一种深度学习算法,用于预测重症患者血清肌素的水平
Ghodsieh Ghanbari1, Jonathan Y Lam1, Supreeth P Shashikumar1
1Department of Biomedical Informatics, University of California San Diego (UCSD) School of Medicine, La Jolla, CA 92093, United States.
JAMIA open
|September 25, 2024
概括
一种深度学习模型Nephrocast准确地预测了重症监护室 (ICU) 患者第二天的血清肌素 (SCr). 该工具有助于识别急性损伤 (AKI) 风险,并告知药物剂量.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 血清肌素 (SCr) 是关键的功能生物标志物,但其变化可能会滞后,特别是在急性损伤 (AKI) 期间.
- 需要预测模型来预测重症患者的功能变化.
研究的目的:
- 开发和验证Nephrocast,这是一个深度学习模型,用于预测成人重症监护室 (ICU) 患者的第二天SCr.
- 用常规收集在ICU的临床数据来评估模型的性能.
主要方法:
- 经过培训和验证的Nephrocast使用成年ICU患者的电子健康记录数据 (2016年1月 - 2024年6月).
- 模型的特征包括人口统计,并发症,生命体征,实验室结果和药物.
- 使用平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 评估的性能,与参考SCr.相比.
主要成果:
- 这项研究包括28,191次接触 (105,718个患者日).
- 内部测试组:中位数MAE为0.09 mg/dL,RMSE为0.15 mg/dL.
- 预期验证:MAE为0.09 mg/dL,RMSE为0.14 mg/dL,表现优于参考SCr.
结论:
- 使用标准ICU临床数据,Nephrocast在预测第二天SCr方面表现强.
- 该模型可以帮助临床医生早期识别和管理AKI风险.
- 潜在的应用包括预测AKI轨迹和指导脏排出药物的剂量.
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