一个基于深度学习的动态模型,用于预测术后患者急性损伤风险严重程度
Esra Adiyeke1, Yuanfang Ren1, Matthew M Ruppert1
1University of Florida Intelligent Critical Care Center, Gainesville, FL; Department of Medicine, Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida, Gainesville, FL.
Surgery
|June 14, 2023
概括
一个新的复发性神经网络模型准确地预测了术后急性损伤 (AKI) 风险. 这种人工智能工具通过提供连续和动态的AKI预测来增强患者护理,改善结果.
科学领域:
- 人工智能在医学中的应用
- 腎臟病學 (nephrology) 是一種醫學.
- 手术患者监测手术患者监测
背景情况:
- 手术后的急性损伤 (AKI) 影响了10-30%的手术患者.
- AKI与医疗保健成本增加,慢性病和死亡率有关.
- 准确及时预测AKI对于患者管理至关重要.
研究的目的:
- 开发和评估一个循环神经网络 (RNN) 模型,用于预测术后的AKI.
- 将RNN模型的性能与传统机器学习模型进行比较.
- 提高临床信任预测模型的可解释性.
主要方法:
- 利用了来自佛罗里达大学卫生部42906名手术患者 (2014-2021) 的数据.
- 开发了一个RNN模型,在24小时内预测AKI风险和阶段.
- 输入包括药物,生命体,实验室结果和历史数据;用于解释性的集成梯度.
主要成果:
- 20%的队列发生了术后的AKI.
- 与物流回归相比,RNN模型在预测所有AKI阶段方面表现出卓越的准确性.
- 曲线下的面积 (AUC) 对于RNN与逻辑回归:没有AKI (0.98与0.93),第一阶段 (0.95与0.81),第二阶段 (2/3) (0.99与0.96).
结论:
- 通过RNN进行时间处理,可以进行细粒度,动态和准确的AKI预测.
- 综合梯度框架提高了模型的可解释性,促进了临床采用.
- 这种人工智能驱动的方法有望改善术后患者的治疗结果.
相关概念视频
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