Abstract
In order to optimize the computer management of smart medical laboratory services and find the optimal solution, we conducted experiments on the laboratory computers of hospitals in this city based on the RBF neural network, which provided references for other researchers. Through the collection of relevant data, this article summarizes and analyzes the existing medical laboratory research, summarizes the existing problems and development directions of the current laboratory, uses the RBF neural network to modify these models, and innovatively achieves a hospital laboratory computer management optimization system with the characteristics of high efficiency, low energy consumption, and fast response. The experimental results prove that the computer management and optimization of laboratory services are optimized through the RBF neural network, and the efficiency of computer management design and optimization is greatly improved. It is about 20% higher than traditional medical laboratory. This shows that the computer management design and optimization of smart medical laboratory services designed by RBF neural network can play an important role in the construction of hospital laboratories.
Highlights
With the proliferation of permanent residents and floating populations, cities will inevitably face many problems during the development process, including resource depletion, pollution, traffic congestion, and ecosystem destruction
The existing medical service system can no longer satisfy the people brought about by economic growth with the increasing demand for medical services; how to solve these problems-system construction based on “smart medical care” may be a feasible solution [1,2,3,4,5,6]. e word “wisdom” of “smart medical care” lies in the fact that its construction method, operation mode, and achieved effects are very different from traditional medical construction
It is clear that the values predicted by the Radial basis function (RBF) algorithm are better than the values predicted by the BP neural network algorithm, which is closer to the real value, especially when it is just run and the number of runs exceeds 600 times. e BP neural network algorithm is only close to the data predicted by RBF when the number of runs is 300 to 500
Summary
With the proliferation of permanent residents and floating populations, cities will inevitably face many problems during the development process, including resource depletion, pollution, traffic congestion, and ecosystem destruction. The existing medical service system can no longer satisfy the people brought about by economic growth with the increasing demand for medical services; how to solve these problems-system construction based on “smart medical care” may be a feasible solution [1,2,3,4,5,6]. E changing trends in library inventory provide an effective decision-making aid for hospital management [11]. The feasibility analysis and demand analysis of the hospital experimental management system are carried out. The database design and overall design of the system are carried out It is divided into five modules: experimental project, contract management, fund management, achievement management, and information release. It is divided into five modules: experimental project, contract management, fund management, achievement management, and information release. e design of the system database is carried out, and the table structure of the project table, project budget table, expense management table, and patent management table is given
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