基于多维度临床数据的肺炎AI病原学类型判别模型

AI pathogen type discrimination model for pneumonia based on multi-dimensional clinical data etiology

目的:基于肺炎患者的临床资料,建立肺炎人工智能(artificial intelligence,AI)病原学类型判别模型,预测肺炎的责任病原体,帮助临床医生选择合适的抗感染治疗方案。方法:回顾性地收集了北京天坛医院急诊科与呼吸科在2018年1月—2020年12月收治的197例肺炎患者的临床资料。选取158例(80%)患者资料作为建模组,构建肺炎AI病原学类型判别模型,39例(20%)作为验证组,验证模型的预测效果。同时,将验证组预测结果与20名急诊科医师的病原学诊断结果进行对比。结果:基于多维度临床数据构建的肺炎AI病原学类型判别模型的病原学验证精度为94.87%。20名急诊科医师病原学诊断的准确率分别为7.69%、15.38%、10.26%、10.26%、15.38%、17.95%、12.82%、10.26%、25.64%、17.95%、7.69%、5.13%、12.80%、20.51%、17.95%、7.69%、28.21%、12.82%、23.08%、15.38%,该模型的验证精度高于临床医师病原学诊断的平均准确率(94.87%vs 14.74%)。结论:借助既往肺炎患者的临床资料,本研究创建了基于多维度临床数据的肺炎AI病原学类型判别模型,该模型可用于早期预测肺炎患者的责任病原体,为临床医生早期制定经验性抗感染治疗方案提供参考。受限于样本量,本模型的临床价值有待进一步研究。

Objective To establish an artificial intelligence(AI)model for pathogen discrimination in pneumonia patients using their medical records.The model aims to predict the causative pathogens of pneumonia and assist 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 diagnoses 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 average 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 pneumonia 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 references 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.