AI pathogen type discrimination model for pneumonia based on multi-dimensional clinical data etiology
Objective:To establish an artificial intelligence(AI)model for pathogen discrimination in pneumo-nia patients using their medical records.The model aims to predict the causative pathogens of pneumonia and as-sist clinical physicians in selecting appropriate antimicrobial treatment strategies.Methods:Retrospective medical records of 197 pneumonia patients admitted to the Emergency and Respiratory Departments of Beijing Tiantan Hospital from January 2018 to December 2020 were collected.Among these,data from 158 patients(80％)were selected to build the pneumonia AI pathogen discrimination model,while data from 39 patients(20％)were used for model validation.The predictive results of the validation group were also compared with the pathogen diagno-ses made by twenty emergency department physicians.Results:The AI pathogen discrimination model,based on multi-dimensional clinical data,achieved a pathogen validation accuracy of 94.87％.In contrast,the accuracy of pathogen diagnosis by the twenty emergency department physicians ranged from 5.13％to 28.21％,with an aver-age accuracy of 14.74％.Therefore,the model's validation accuracy outperformed the average accuracy of clinical physician pathogen diagnoses(94.87％vs 14.74％).Conclusion:By utilizing historical medical records of pneu-monia patients,this study successfully developed an AI pathogen discrimination model based on multi-dimensional clinical data.The model shows promise in early prediction of pneumonia pathogens and provides valuable refer-ences for clinical physicians in selecting empirical antimicrobial treatment strategies.However,due to limitations in sample size,further research is warranted to explore the full clinical potential of this model.
NETL
NSTL
万方数据
