Development and Validation of an Explainable Machine Learning Model for Predicting Invasive Fungal Infection in Acute-On-Chronic Liver Failure Within 28 Days.

Background and objective: Acute-on-chronic liver failure (ACLF) is associated with significantly higher short-term mortality, and the presence of invasive fungal infection (IFI) further increases this risk. This study aims to develop a ML model that predicts the risk of IFI in ACLF patients.

Methods: This study included 1112 patients divided into a training set and a validation set, with another 188 patients serving as an external validation cohort. The Recursive Feature Elimination (RFE) method was used to select the most significant variables for model development. Four machine learning algorithms were compared to identify the optimal model. The models were evaluated and compared using C-index, time-dependent ROC curves, decision curve analysis (DCA), and calibration curves. The LIME (Local Interpretable Model-Agnostic Explanations) method was used to identify the high-risk populations utilised by the model.

Results: 778 patients were included in the training set, 334 in the internal validation set, and 188 in the external validation set. The study found that Random Forest (RF) was the best-performing ML algorithm. In the training set, the RF model achieved an AUROC of 0.922 (0.911-0.933), significantly higher than MELD (0.854, 0.835-0.873, p < 0.001), CLIF-C OF (0.753, 0.724-0.783, p < 0.001), and CLIF-C ACLF (0.879, 0.863-0.896, p = 0.020). The same trend was observed in both the internal and external validation sets. The time-dependent ROC curve showed that the RF model outperformed the other scores for predicting the risk of IFI in 28 days. DCA and calibration curves also demonstrated superior clinical benefits for the RF model across all datasets. LIME revealed bacterial infection (BI), Na < 136 mmol/L, CRP (C-reactive protein) > 20.1 g/L, and TBIL(Total Bilirubin) > 196.7 μmol/L as the high-risk groups.

Conclusion: The RF model effectively predicts the risk of IFI in ACLF patients. The application of LIME enables the identification of high-risk populations, providing clinical value for patient management.