AI Article Synopsis
- The study aimed to create an artificial neural network model to predict the risk of nosocomial (hospital-acquired) infections.
- Utilizing clinical data from over 27,000 patients, the model achieved a high prediction fit rate of 0.9891 and an area under the ROC curve of 0.986, indicating strong accuracy.
- The findings suggest that this neural network model can serve as a valuable tool to aid in the diagnosis and management of nosocomial infections.
Article Abstract
Objective: To establish a model based on artificial neural network in the prediction of nosocomial infection risk.
Methods: Clinical data of 27,352 inpatients extracted from hospital information system were cleaned and coded, and the model of prediction in nosocomial infection risk was developed based on artificial neural network.
Results: The structure of artificial neural network is {16-6-1}-BP, and the fit rate of prediction was 0.9891. The area under ROC curve was 0.986.
Conclusion: Artificial neural network model can be used as a tool for nosocomial infection forecasting, which can provide supplementary information for the diagnosis and control of nosocomial infection.
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