痴呆症护理研究和心理社会因素
Sita Radhakrishnan1, Jagath Prasad Sreedhar G2
1SAINTGITS College of Engineering, KOTTAYAM, KERALA, India.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 24, 2025
概括
开发了一种人工神经网络 (ANN) 模型,用于自动调节需要通风的痴呆症患者的氧气水平. 该系统实现了86%的准确性,帮助临床医生提供关键的呼吸支持.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 神经学 神经学
背景情况:
- 痴呆症患者有高风险的呼吸衰竭,可能导致死亡.
- 持续的呼吸辅助对于管理痴呆症呼吸道损害至关重要.
- 需要一个自动化系统来保持最佳的血液氧和水平 (SaO2).
研究的目的:
- 开发一个使用人工神经网络 (ANN) 的自动呼吸器支持系统.
- 预测和调节痴呆和呼吸系统问题患者的启发氧气 (FiO2).
- 将ANN模型的预测与医生评估进行比较.
主要方法:
- 收集了325名急性呼吸系统问题患者的数据,包括痴呆症患者.
- 利用实时患者数据 (动脉血液气体分析,脉冲氧计,呼吸机设置) 在R编程中进行参数识别.
- 在 MATLAB 中开发了一个 ANN 模型来预测 FiO2,并将其准确性与医生预测进行比较.
主要成果:
- 经过训练的ANN模型实现了0.019907的平均平方误差和0.852.7的R值.
- 在一个单独的数据集上测试时,ANN模型显示了86%的准确性,超过了医生预测.
- 通过调整隐藏层大小以减少过度拟合,优化了模型性能.
结论:
- 对于易患呼吸道疾病的痴呆症患者来说,持续监测和控制氧和度至关重要.
- 在 MATLAB 中开发的 ANN 模型可以有效地预测生命支持的灵感氧气水平.
- 拟议的ANN模型是临床医生的一种有价值的辅助工具,在氧气水平预测中提供了80%以上的准确性.
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