Explore the Application of Artificial Intelligence in the Hospital Infectious Disease Early Warning System
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.
Infectious diseasesartificial intelligenceearly warning system
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