Medical Equipment Innovation Research Center, Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
Sichuan University - Pittsburgh Institute, Sichuan University, Chengdu, 610207, China.
BMC Med Inform Decis Mak. 2023 Aug 25;23(1):166. doi: 10.1186/s12911-023-02267-4.
Large-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model.
We extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models.
The results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset.
The proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.
大型医疗设备在医疗服务中得到广泛应用，对诊断至关重要，但容易出现各种异常和故障。大多数进行定期维护的医院多年来一直受到医疗设备相关事件的困扰。目前，医疗物联网（IoMT）已成为监测医疗设备实时状态的重要工具。在本文中，我们开发了四川大学华西医院的计算机断层扫描（CT）设备的 IoMT 系统，并收集了系统状态时间序列数据。提出了新颖的多元时间序列分类模型和框架来预测 CT 设备的异常。通过模型确定了与设备异常密切相关的重要特征。
我们从 IoMT 中提取了 2020 年 5 月 19 日至 2021 年 5 月 19 日期间 11 台设备的实时 CT 设备状态时间序列数据，其中包括设备油温、阳极电压等。由于与成像质量下降和 CT 设备故障有关，电弧被识别为异常的标签。为了提高预测准确性，使用滑动时间窗口中的原始历史时间序列数据段的统计信息和变换来构建新特征。由于时间序列数据的特殊性，提出了两种用于分割训练集和测试集的框架。然后使用决策树、支持向量机、逻辑回归、朴素贝叶斯和 K 最近邻分类模型对系统状态进行分类。我们还将我们的模型与最先进的模型进行了比较。
结果表明，我们的方法的异常预测准确性和召回率分别为 79%和 77%。油温和阳极电压被确定为可能导致异常的决定性特征。基于我们的数据集，所提出的模型在预测 CT 设备的异常方面优于其他模型。
所提出的方法可以预测 CT 设备的状态，并可作为实际维护的参考，以减少医疗设备的意外异常。它还为如何处理实际情况下非均匀和不平衡的时间序列数据提供了新的见解。
