Construction of a Biosafety Competency Evaluation Index System for Emergency Medical Service Nursing Personnel and Comprehensive Evaluation Using PSO-BP Neural Network

Corresponding author: yangzhuoyi, 18236011@qq.com

DOI: 10.12201/bmr.202606.00005

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  Abstract: Abstract: Purpose/Significance The biosafety competency of nursing personnel in emergency medical service teams is directly related to the quality of emergency rescue missions in public health emergencies, major infectious disease outbreaks, and biosafety incidents. However, studies on the application of intelligent algorithms to comprehensive evaluation in this field remain limited. To construct a biosafety competency evaluation index system for nursing personnel in emergency medical service teams, and to explore the fitting effect of the PSO-BP neural network on AHP comprehensive evaluation results, providing a methodological foundation for developing evaluation tools in this field. Method/Process An evaluation index system was constructed through literature analysis, expert interviews, and the Delphi method. The Analytic Hierarchy Process (AHP) was used to determine indicator weights and calculate comprehensive evaluation values. A PSO-BP neural network model was established using AHP comprehensive evaluation values as expected outputs for network training. From 203 datasets, 153 were randomly selected as the training set, 25 as the validation set, and 25 as the test set for model validation. Result/Conclusion The constructed evaluation index system, comprising 4 dimensions and 13 indicators, passed expert consistency testing with an expert authority coefficient of 0.91. The fitting error between the PSO-BP neural network output values and the AHP comprehensive evaluation values was less than 2%, and the coefficient of determination R2 approached 1, indicating that the model could fit AHP comprehensive evaluation results well. The evaluation index system constructed in this study provides a structured tool for comprehensive evaluation in this field. Building on this foundation, the PSO-BP neural network model demonstrated good fitting performance against AHP comprehensive evaluation results, providing a methodological foundation for extending the evaluation model with objective data and developing evaluation tools in the future.

  Key words: particle swarm optimization; BP neural network; emergency medical service nursing personnel; biosafety; comprehensive evaluation

  Submit time: 2 June 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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