摘要
目的 构建传染病预警系统,以实现及早及时发现疫情暴发。方法 以流行性感冒为例,利用历史确诊数据以及患者的现住址地理信息,通过构建长短期记忆网络(LSTM)预测模型来预测未来的病例数量,同时运用K-means聚类模型分析病例的空间聚集性,探索人工智能在医院传染病预警系统中的应用。结果 LSTM预测模型可对流行性感冒患者数量进行预测,从而为医院在传染病防治方面提前采取措施提供有力依据。此外,K-means聚类模型可对患者进行聚集性分析,发现不同地区患者的分布情况及可能暴发流行的趋势。结论 借助人工智能技术,可以构建一套高效且精准的传染病预警系统,从而为医院提供及时可靠的传染病预警信息。
Objective An early warning system for infectious disease is built to realize early and timely detection of outbreaks.Methods Take influenza as an example,the use of historical diagnostic data and the patient address geographic information,using the long short-term memory network(LSTM)prediction model to predict the number of future cases,at the same time using K-means clustering model analysis of spatial clustering,explore the application of artificial intelligence in the hospital infectious disease early warning system.Results The LSTM prediction model can realize the ability to predict the number of influenza patients,thus providing a strong basis for hospitals to take advance measures in the prevention and treatment of infectious diseases.In addition,the K-means clustering model can conduct the clustering analysis of patients,andfind the distribution of patients and the trend of possible outbreaks in different regions.Conclusion With the help of artificial intelligence technology,an efficient and accurate infectious disease early warning system can be built,so as to provide timely and reliable early warning information of infectious diseases.
作者
夏胡
朱海
XIA Hu;ZHU Hai(Department of prevention and health,The Ningbo No.2 Hospital,Ningbo 315010,Zhejiang,China)
出处
《中国卫生信息管理杂志》
2024年第4期571-577,共7页
Chinese Journal of Health Informatics and Management
基金
浙江省卫生健康委医药卫生科技计划项目“医院人工智能技术在聚集性病例识别及预警中的应用”(2021KY1011)。
关键词
传染病
人工智能
预警系统
Infectious diseases
artificial intelligence
early warning system
