Applying machine learning algorithms to electronic health records to predict pneumonia after respiratory tract infection

逻辑回归 推车 医学 肺炎 呼吸道感染 机器学习 社区获得性肺炎 人口 随机森林 人工智能 内科学 计算机科学 呼吸系统 机械工程 环境卫生 工程类
作者
Xiaohui Sun,Abdel Douiri,Martin Gulliford
出处
期刊:Journal of Clinical Epidemiology [Elsevier BV]
卷期号:145: 154-163 被引量:18
标识
DOI:10.1016/j.jclinepi.2022.01.009
摘要

Objectives To predict community acquired pneumonia after respiratory tract infection (RTI) consultations in primary care by applying machine learning to electronic health records. Study design and Setting A population-based cohort study was conducted using primary care electronic health records between 2002 to 2017. Sixteen thousand two hundred eighty-nine patients who consulted with RTIs then subsequently diagnosed with pneumonia within 30 days were compared with a random sample of eligible RTI patients. Variable selection compared logistic regression, random forest and penalized regression models. Prediction models were developed using classification and regression trees (CART) and logistic regression. Model performance was assessed through internal and temporal validations. Results Older age, comorbidity, and initial presentation with lower respiratory tract infection (LRTIs) were identified as the main predictors of pneumonia diagnosis. Developed models achieved good discrimination accuracy with AUROC for the logistic regression model being 0.81 (0.80, 0.84) and 0.70 (0.69, 0.71) for CART during internal validation, and 0.80 (0.79, 0.81) vs. 0.68 (0.67, 0.69) for temporal validation. Conclusion From a large number of candidate variables, a small number of predictors of pneumonia were consistently identified through machine learning variable selection procedures. Logistic regression generally provided better model performance than CART models.
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