Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamlit interface to generate patient-specific explanations and rate models on accuracy, separability, stability, response time, understandability, and user experience. Random Forest combined with SHAP consistently achieved the highest rankings in Borda count. Clinicians reported reduced evaluation time, enhanced explanation clarity, and increased confidence in model outputs. These results demonstrate that CDSS-EQCM can effectively streamline interpretability assessment and support clinician decision-making in medical diagnostics. Future work will focus on deeper electronic medical record integration and interactive parameter tuning to further enhance real-time diagnostic support.

Background/Objectives: Clostridioides difficile infection (CDI) represents an important healthcare-associated complication in hospitalized patients, particularly in those exposed to antibiotics, prolonged hospitalization, and intensive treatment during COVID-19. This study aimed to design, evaluate, and interpret machine learning models for predicting CDI occurrence in hospitalized COVID-19 patients across individual pandemic waves, with respect to administered treatment and clinical characteristics. Methods: Anonymized clinical data from 3848 COVID-19-positive patients treated at the University Hospital of L. Pasteur in Košice, Slovakia, were analyzed following the CRISP-DM methodology. Four classification models were compared: logistic regression, Random Forest, XGBoost, and a multilayer perceptron. Missing values were addressed using MICE and KNN imputation, and class imbalance was handled through oversampling techniques. Given the low CDI prevalence of 2.68%, model performance was primarily assessed using the precision–recall area under the curve (PR-AUC), with AUROC reported for comparability. Interpretability was supported using SHAP, LIME, and odds ratio analysis. Results: The best-performing models achieved PR-AUC values up to 0.160, representing more than a fivefold improvement over the random baseline of 0.027. XGBoost reached the highest AUROC of 0.823, followed by Random Forest with 0.798. Inflammatory markers were identified as important predictors of CDI risk. A Flask-based decision-support web application was developed to provide CDI risk estimation with patient-specific explanations. A preliminary pilot usability evaluation involving two physicians yielded a mean System Usability Scale score of 73.75; however, the very small evaluator sample limits the generalizability of this finding. Conclusions: Interpretable machine learning models can support clinically meaningful CDI risk stratification in highly imbalanced COVID-19 hospital datasets. The proposed decision-support tool shows potential for future integration into clinical workflows, although external and prospective validation is required.

Objective—To identify effective data analytics and machine learning solutions that can help in the decision-making process in the medical domain and contribute to the understanding of COVID-19 disease. In this study, we analyze data from anonymized electronic medical records of 4711 patients with COVID-19 disease admitted to hospital in Atlanta. Methods—We used random forest, LightGBM, XGBoost, CatBoost, KNN, SVM, logistic regression, and MLP neural network models in this work. The models are evaluated depending on the type of prediction by relevant metrics, especially accuracy, F1-score, and ROC AUC score. Another aim of the work was to find out which factors most affected severity and mortality risk among the patients. To identify the important features, different statistical methods were used, as well as LASSO regression, and explainable artificial intelligence (XAI) method SHAP values for model explainability. The best models were implemented in a web application and tested by medical experts. The model for prediction of mortality risk was tested on a validation cohort of 45 patients from the Department of Infectiology and Travel Medicine, L. Pasteur University Hospital in Košice (UNLP). Results—Our study shows that the best model for predicting COVID-19 disease severity was the LightGBM model with accuracy of 88.4% using all features and 89.5% using the eight most important features. The best model for predicting mortality risk was also the LightGBM model, which achieved a ROC AUC score of 83.7% and a classification accuracy of 81.2% using all features. Using a simplified model trained on the 15 most important features, the ROC AUC score was 83.6% and the classification accuracy was 80.5%. We deployed the simplified models for predicting COVID-19 disease severity and for predicting the risk of COVID-19-related death in a web-based application and tested them with medical experts. This test resulted in a ROC AUC score of 83.6% and an overall prediction accuracy of 73.3%.

Backgroud/Objectives: Clostridioides difficile infection (CDI) remains a major problem for healthcare facilities. The COVID-19 pandemic was associated with increased hospitalizations, frequent antibiotic use, and changes in infection prevention and control regulations, which may have influenced the epidemiological and clinical characteristics of CDI. The aim of this study was to evaluate whether a history of COVID-19 was associated with clinical characteristics, treatment exposure, recurrence, ribotype distribution, and mortality in patients with CDI. Methods: This retrospective study included patients diagnosed with CDI during the period 2020–2022 at the Louis Pasteur University Hospital in Košice, Slovakia. We analyzed 324 patients, of whom 172 (53.1%) had no history of COVID-19 and 144 (44.4%) had previous or concurrent COVID-19, defined as documented infection within three months before or at the time of CDI diagnosis. Eight patients (2.5%) developed COVID-19 after CDI diagnosis and were excluded from the comparative analyses. Results: Patients with previous or concurrent COVID-19 had higher exposure to PPIs (88.2% vs. 73.3%; p = 0.0009), corticosteroids (70.1% vs. 26.2%; p < 0.0001), cephalosporins (72.9% vs. 61.6%; p = 0.034) and macrolides (37.5% vs. 20.3%; p = 0.0007). Overall antibiotic exposure was high (93.8%) and did not differ significantly between patients with previous or concurrent COVID-19 and those without COVID-19 (93.8% vs. 94.2%; p = 0.871). Previous or concurrent COVID-19 was not associated with increased 90-day mortality after CDI (42.4% vs. 43.6%; p = 0.824). Conclusions: Our findings suggest that a history of COVID-19 was associated with the risk factor profile, recurrence rate and the molecular epidemiology of CDI, rather than with a more severe clinical course or worse prognosis.

Cytomegalovirus (CMV) is a ubiquitous herpesvirus infecting a large proportion of the population worldwide. Clinical manifestations range from asymptomatic infection to severe disease with multi-organ involvement, depending primarily on host immune status. CMV hepatitis is an uncommon clinical presentation. In pregnancy, infection is usually asymptomatic in immunocompetent women; however, transplacental transmission may occur, leading to congenital infection. Congenital CMV is the most common congenital viral infection. This report describes the case of a 30-year-old pregnant woman with acute CMV infection acquired during the first trimester. The disease manifested as hepatitis, with ALT and AST levels reaching up to 31-fold and 15-fold above the upper limits of normal, respectively. Gradual improvement was observed with supportive treatment, and the outcome was favourable for both mother and child. The infant was delivered at term and has shown normal development; at 8 years of age, the only reported condition is lactose intolerance.