التفاصيل البيبلوغرافية
| العنوان: |
Anomaly prediction of CT equipment based on IoMT data |
| المؤلفون: |
Wang, Changxi, Liu, Qilin, Zhou, Haopeng, Wu, Tong, Liu, Haowen, Huang, Jin, Zhuo, Yixuan, Li, Zhenlin, Li, Kang |
| المساهمون: |
National Natural Science Foundation of China, Natural Science Foundation of Sichuan, China, Med-X for informatics, Sichuan University, National Key Research and Development Program of China, the 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University |
| المصدر: |
BMC Medical Informatics and Decision Making ; volume 23, issue 1 ; ISSN 1472-6947 |
| بيانات النشر: |
Springer Science and Business Media LLC |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Health Informatics, Health Policy, Computer Science Applications |
| الوصف: |
Background 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. Methods 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. Results 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 ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| DOI: |
10.1186/s12911-023-02267-4 |
| DOI: |
10.1186/s12911-023-02267-4.pdf |
| DOI: |
10.1186/s12911-023-02267-4/fulltext.html |
| الإتاحة: |
https://doi.org/10.1186/s12911-023-02267-4Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test ; https://creativecommons.org/licenses/by/4.0Test |
| رقم الانضمام: |
edsbas.2AB1E6A7 |
| قاعدة البيانات: |
BASE |
