改进医疗器械管理的机器学习:专注于透析机器
Mato Martinović1, Milena Kosović1, Lemana Spahić2,3
1Faculty of Information Systems and Technologies, University of Donja Gorica, Oktoih 1, 81000 Podgorica, Montenegro.
概括
机器学习模型可以预测透析机器的性能和错误,减少患者死亡和维修成本. 这种预测性维护方法提高了治疗质量,并帮助临床工程部门.
科学领域:
- 生物医学工程 生物医学工程
- 医疗技术管理 医疗技术管理
- 医疗保健中的机器学习
背景情况:
- 透析对数百万人来说是一个关键的治疗方法,但透析机器的技术错误导致大约10%的患者死亡.
- 使用机器学习的预测性维护提供了一个解决方案,以减轻与透析设备故障相关的风险.
研究的目的:
- 用各种回归模型预测透析机的性能状态,并识别使用各种回归模型的潜在错误.
- 通过先进的技术管理,提高透析治疗的可靠性和安全性.
主要方法:
- 一个七步方法,包括数据收集,预处理 (1034次测量),模型选择,培训,评估,微调和预测.
- 训练了12个机器学习模型,以预测机器性能,温度和导电率错误.
主要成果:
- 后勤回归在预测透析机器性能方面显示了最高的准确性.
- 支持矢量机 (SVM) 在温度和导电性预测的测试数据集上实现了最低的平均平方误差 (MSE).
- 各种模型在预测特定参数方面表现出有效性,拉索和线性回归在温度方面表现出强的表现,电导度方面表现出决策树.
结论:
- 该研究强调了自动化系统在临床工程和卫生技术部门内用于高级透析机器管理的潜力.
- 实施这些机器学习模型可以显著提高护理质量,并降低与透析治疗相关的运营成本.
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