摘要
医疗设备运维状态全景感知是智慧医疗落地实施的基础性保障,提出了一种基于机器学习算法的医疗设备运维状态自主感知及主动预警模型。引入深度学习多维感知医疗设备多源异构故障数据训练样本特征.实现医疗设备运维状态自主感知.引入强化学习实现测试样本故障特征自主决策.构建医疗设备故障主动预警机制。以该院设备科为模型效能验证载体.对模型开展效能仿真验证.结果表明模型具有故障信息感知全面.医疗设备兼容性强.主动预警有效率高等优势。
The panoramic perception of medical equipment operation and maintenance status is the basic guarantee for the implementation of smart medical care,the machine learning algorithm-based autonomous perception and active early warning model of medical equipment operation and maintenance status is proposed.Introduce deep learning multi-dimensional perception of medical equipment multi-source heterogeneous fault data training sample characteristics to realize autonomous perception of medical equipment operation and maintenance status,introduce reinforcement learning to realize autonomous decision-making of test sample fault characteristics,and build the active early warning mechanism for medical equipment faults.Taking the equipment department of hospital as the carrier of model effectiveness verification,the effectiveness simulation of the model was carried out,the results show that the model has the advantages of comprehensive fault information perception,strong compatibility of medical equipment,high efficiency of active early warning.
作者
马玉春
秦航
殷小进
MA Yuchun;QIN Hang;YIN Xiaojin(Equipment Department of Xuyu People's Hospital of Jiangsu Province,Huai'an,211700;Department of Medical Equipment,Nanjing First Hospital of Jiangsu Province,Nanjing,210006)
出处
《中国医疗器械杂志》
2021年第5期580-584,共5页
Chinese Journal of Medical Instrumentation
基金
江苏省中医药科技发展计划项目(YB201937)
江苏省卫生健康委员会医学科技发展项目(YKK17073)。
关键词
机器学习算法
医疗设备
运维状态
自主感知与主动预警
仿真验证
machine learning algorithm
medical equipment
operation and maintenance status
autonomous perception and active warning
simulation verification
