使用基于人体组成信息的人工神经网络模型,预测维护血液透析患者的超过量
Jiaoyan Chen1, Jurong Yang1, Xianqiong Lu1
1Department of Nephrology, The Third Affiliated Hospital of Chongqing Medical University (FangDa Hospital), No. 1, Shuanghu Branch Road, Yubei District, Chongqing, 401120, China.
BMC medical informatics and decision making
|November 11, 2025
概括
精确的流体管理对于血液透析患者至关重要. 一个人工神经网络模型有效地利用透析前的身体组成预测超过量,改善了患者的护理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 准确的液体状况评估对于维护血液透析 (MHD) 患者至关重要.
- 目前用于超过 (UF) 体积评估的临床方法不足.
- 需要快速准确的工具来评估MHD患者的UF.
研究的目的:
- 开发和验证人工神经网络 (ANN) 模型,用于预测MHD患者的UF体积.
- 评估透析前身体组成对MHD患者的UF的影响.
主要方法:
- 从319名MHD患者中创建了一个包含1205条条目的数据集.
- 使用生物阻抗测量身体成分;脏病学家设定了UF目标.
- 使用MATLAB开发了一个逆向传播神经网络 (BPNN) 模型,以根据身体组成预测UF.
主要成果:
- 该ANN模型实现了高预测性能,R2 = 0.965.
- 观察到和预测的UF体积之间的平均差异为0.182 L.
- 透析前的主要身体组成指标显著影响了UF的结果.
结论:
- 一个使用透析前身体组成的ANN模型有效地预测了MHD患者的UF体积.
- 该模型为优化MHD中UF评估和管理提供了有价值的工具.
- 这项研究强调了体质成分在透析患者的流体管理中的重要性.
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