Development of an Open-Access and Explainable Machine Learning Prediction System to Assess the Mortality and Recurrence Risk Factors of Clostridioides Difficile Infection Patients

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence facilitates prevention, timely treatment, and improves clinical outcomes. The aim herein is to establish an open-access web-based prediction system, which estimates CDI patients' mortality and recurrence outcomes and explains machine learning prediction with patients' characteristics. Prognostic models are developed using four various types of machine learning algorithms and the statistical logistics regression model utilizing over 15000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm gradient boosting machine (GBM) (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperforms statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. As the difficulty to interpret complex machine learning results limits their use in the medical area, Shapley additive explanations (SHAP) are adapted to identify which features are crucial to the machine learning models and associate them with clinical findings. SHAP analysis shows that older age, reduced albumin levels, higher creatinine levels, and higher white blood cell count are the most highly associated mortality features, which is consistent with existing clinical findings. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at .

Automated summary

A prediction system utilizing machine learning algorithms and statistical models has been established to estimate the mortality and recurrence risk of CDI patients. Features such as age, albumin levels, creatinine levels, and white blood cell count were found to be closely associated with mortality.