Research on Self-perception and Active Warning Model of Medical Equipment Operation and Maintenance Status Based on Machine Learning Algorithm.
10.3969/j.issn.1671-7104.2021.05.023
- Author:
Yuchun MA
1
;
Hang QIN
2
;
Xiaojin YIN
1
Author Information
1. Equipment Department of Xuyu People's Hospital of Jiangsu Province, Huai'an, 211700.
2. Department of Medical Equipment, Nanjing First Hospital of Jiangsu Province, Nanjing, 210006.
- Publication Type:Journal Article
- Keywords:
autonomous perception and active warning;
machine learning algorithm;
medical equipment;
operation and maintenance status;
simulation verification
- MeSH:
Algorithms;
Computer Simulation;
Machine Learning;
Self Concept;
Surgical Equipment
- From:
Chinese Journal of Medical Instrumentation
2021;45(5):580-584
- CountryChina
- Language:Chinese
-
Abstract:
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.
