Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients.

This is a retrospective cohort study conducted during 2015–2017. All inpatients tested for C. difficile were included. CDI was defined as having a positive glutamate dehydrogenase and toxin results. We restricted analyses to the first record of C. difficile testing per patient. Of 3514 patients tested, 136 (4%) had CDI. Age and antibiotic use within 90 days before C. difficile testing were associated with CDI (P < 0.01). We tested 10 ML methods with and without resampling. Logistic regression, random forest and naïve Bayes models yielded the highest AUC ROC performance: 0.6. Predicting CDI was difficult in our cohort of patients tested for CDI. Multiple ML models yielded only modest results in a real-world population of hospitalized patients tested for CDI.

先前的研究表明，用于预测健康结果的机器学习（ML）模型的结果令人鼓舞。我们开发和测试ML模型，以预测住院患者的艰难梭菌感染（CDI）。

这是2015-2017年期间进行的一项回顾性队列研究。包括所有接受艰难梭菌检测的住院患者。CDI被定义为具有阳性的谷氨酸脱氢酶和毒素结果。我们将分析限制为每位患者艰难梭菌测试的第一记录。在接受测试的3514位患者中，有136位（4％）患有CDI。艰难梭菌检测前90天内的年龄和抗生素使用与CDI有关（P <0.01）。我们测试了10种带有或不带有重采样的ML方法。Logistic回归，随机森林和朴素贝叶斯模型产生的AUC ROC性能最高：0.6。在我们的接受CDI测试的患者队列中，很难预测CDI。在现实世界中接受CDI测试的住院患者中，多种ML模型仅产生了适度的结果。