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

Abstract

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 15 000 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 https://www.cdiml.care/.

Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning

“…Then, we selected the top n features when their cumulative normalized SHAP values account for more than 90% of the sum of all normalized SHAP values, i.e., , and retrained the models based on the selected important features. In this way, the redundant features can be excluded to reduce the model complexity [ 19 ].…”

“…For example, Wang et al proposed an explainable machine learning framework based on SHAP for intrusion detection systems [ 18 ]. Ng et al assessed the mortality and recurrence risk factors of clostridioides difficile infection patients using an explainable machine learning prediction system based on SHAP [ 19 ]. SHAP quantifies the contribution of each player (feature) in a collaborative game (the machine learning model) [ 16 ].…”

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

“…To validate the aforementioned machine learning algorithms to construct prediction models of the different emotions, samples were randomly split into two groups to generate the training dataset and the test dataset (55-57) and subjected to cross-validation. To select the most appropriate cross-validation method, k-fold cross-validation (KCV) (58-60) (test size = 0, k = 5), GridSearch Cross-validation (GridSearchCV) (61)(62)(63), and RandomizedSearch Crossvalidation (RandomizedSearchCV) (64)(65)(66) were tested in a preliminary study. RandomizedSearchCV provided the highest accuracy with the fastest calculation time.…”