Anomaly prediction of CT equipment based on IoMT data

BMC Medical Informatics and Decision Making

Research

Haopeng Zhou¹  Zhenlin Li²  Jin Huang³  Haowen Liu³  Yixuan Zhuo³  Kang Li³  Tong Wu³  Qilin Liu³  Changxi Wang⁴ 

[1] College of Electrical Engineering, Sichuan University, 610065, Chengdu, China;Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, China;Medical Equipment Innovation Research Center, Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, 610041, Chengdu, China;Medical Equipment Innovation Research Center, Biomedical Big Data Center, Med-X Center for Informatics, West China Hospital, Sichuan University, 610041, Chengdu, China;Sichuan University - Pittsburgh Institute, Sichuan University, 610207, Chengdu, China;

关键词: Anomaly prediction;    CT equipment;    Internet of Medical Things;    Multivariate time series Classification;    Maintenance strategy;   

DOI  :  10.1186/s12911-023-02267-4

 received in 2022-08-10, accepted in 2023-08-17,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundLarge-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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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