基于深度学习的疾控中心电子信息系统智能化优化研究

Research on Intelligent Optimization of Electronic Information Systems in Centers for Disease Control and Prevention Based on Deep Learning

随着社会的不断发展,面对日益增长的数据量和日趋复杂的公共卫生问题,传统的信息系统已难以满足快速准确处理信息的需求,从而影响了公共卫生处理的效率。因此,该文通过整合最先进的深度学习模型,对疾控中心电子信息系统智能化的优化策略进行分析研究。研究结果表明,与现有系统相比,所提出的优化方案显著提升了信息处理的准确性和时效性,为健康风险评估和资源分配提供了更加可靠的科学依据。

With the continuous development of society,facing the increasing amount of data and increasingly complex public health problems,traditional information systems are no longer able to meet the needs of fast and accurate information processing,thereby affecting the efficiency of public health processing.Therefore,this article analyzes and studies the optimization strategies for the intelligence of electronic information systems in disease control centers by integrating the most advanced deep learning models.The research results indicate that the proposed optimization scheme significantly improves the accuracy and timeliness of information processing compared to existing systems,providing a more reliable scientific basis for health risk assessment and resource allocation.