探索人工智能在医院传染病预警系统中的应用
Explore the Application of Artificial Intelligence in the Hospital Infectious Disease Early Warning System
夏胡 1朱海2
作者信息
- 1. 宁波市第二医院防保科,浙江省宁波市,315010
- 2. 宁波市第二医院信息科,浙江省宁波市,315010
- 折叠
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.
Abstract
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,and find 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.
关键词
传染病/人工智能/预警系统Key words
Infectious diseases/artificial intelligence/early warning system引用本文复制引用
基金项目
浙江省卫生健康委医药卫生科技计划项目(2021KY1011)
出版年
2024
NETL
NSTL
万方数据