Issues related to artificial intelligence (AI) and ethics have gained much traction worldwide. The impact of AI on society has been extensively discussed. This study presents a bibliometric analysis of research results, citation relationships among researchers, and highly referenced journals on AI and ethics on a global scale. Papers published on AI and ethics were recovered from the Microsoft Academic Graph Collection data set, and the subject terms included “artificial intelligence” and “ethics.” With 66 nations’ researchers contributing to AI and ethics research, 1585 papers on AI and ethics were recovered, up to 5 July 2021. North America, Western Europe, and East Asia were the regions with the highest productivity. The top ten nations produced about 94.37% of the wide variety of papers. The United States accounted for 47.59% (286 articles) of all papers. Switzerland had the highest research production with a million-person ratio (1.39) when adjusted for populace size. It was followed by the Netherlands (1.26) and the United Kingdom (1.19). The most productive authors were found to be Khatib, O. (n = 10), Verner, I. (n = 9), Bekey, G. A. (n = 7), Gennert, M. A. (n = 7), and Chatila, R., (n = 7). Current research shows that research on artificial intelligence and ethics has evolved dramatically over the past 70 years. Moreover, the United States is more involved with AI and ethics research than developing or emerging countries.

Background/Objectives: Patients with end-stage renal disease (ESRD) are at an increased risk of major adverse cardiac events (MACEs), highlighting the need for accurate risk prediction and personalized interventions. This study aims to develop and evaluate machine learning (ML) models to identify key predictive features and enhance clinical decision-making in MACE risk assessment. Methods: A dataset comprising 84 variables, including patient demographics, laboratory findings, and comorbidities, was analyzed using CatBoost, XGBoost, and LightGBM. Feature selection, cross-validation, and SHAP (SHapley Additive exPlanations) analyses were employed to improve model interpretability and clinical relevance. Results: CatBoost exhibited the highest predictive performance among the models tested, achieving an AUC of 0.745 (0.605–0.83) with balanced sensitivity and specificity. Key predictors of MACEs included antiplatelet use, the grade of left ventricular hypertrophy, and serum albumin levels. SHAP analysis enhanced the interpretability of model outputs, supporting clinician-led risk stratification. Conclusions: This study highlights the potential of ML-based predictive modeling to improve MACE risk assessment in patients with ESRD. The findings support the adoption of ML models in clinical workflows by integrating explainable AI methods to enable individualized treatment planning. Future integration with electronic health record systems may facilitate real-time decision-making and enhance patient outcomes.

Introduction: Long-term heart failure and mortality after catheter ablation for premature ventricular contraction (PVC) remain underexplored. Methods: We retrospectively analyzed 4195 adults who underwent PVC ablation in a nationwide claims database. To address class imbalance, we used synthetic minority over-sampling technique (SMOTE) and random over-sampling examples (ROSE). Five supervised algorithms were compared: logistic regression, decision tree, random forest, XGBoost, and LightGBM. Discrimination was assessed by stratified five-fold cross-validation using the area under the receiver operating characteristic curve (ROC AUC). Because rare events can bias ROC, we also examined precision–recall (PR) curves. Results: For predicting three-year heart failure, LightGBM with ROSE achieved the highest ROC AUC at 0.822. For three-year mortality, logistic regression with ROSE and LightGBM with ROSE showed balanced performance with ROC AUCs of 0.886 and 0.882. Pairwise DeLong tests indicated that these leading models formed a high-performing cluster without significant differences in ROC AUC. Age, prior heart failure, malignancy, and end-stage renal disease were the most influential predictors by model explainability analysis. Discussion: Addressing class imbalance and benchmarking modern learners against a transparent logistic baseline yielded robust, clinically interpretable risk stratification after PVC ablation. These models are suitable for integration into electronic health record dashboards, with external validation and local threshold optimization as next steps.

Purpose: Frequent premature ventricular complexes are increasingly recognized as a contributor to ventricular dysfunction and progression to heart failure. Current clinical guidelines endorse radiofrequency catheter ablation as an effective treatment for symptomatic or drug-refractory premature ventricular complexes. However, nationwide long-term real-world outcomes, particularly those related to post-ablation medication de-escalation, remain insufficiently characterized. Patients and Methods: Using Taiwan’s National Health Insurance Research Database, we conducted a nationwide retrospective cohort study of adults who underwent first-time radiofrequency catheter ablation for premature ventricular complexes between 2002 and 2018, with follow-up extending to December 31, 2021, allowing for up to three years of observation. Clinical outcomes included all-cause mortality and heart failure rehospitalization. Antiarrhythmic drug utilization was assessed through both prescription prevalence and defined daily dose, evaluated at baseline, 90 days, and 1 year post-ablation. Subgroup analyses were stratified by baseline heart failure status. Results: A total of 4195 patients (mean age 53± 15 years; 49% women) were included, of whom 7% had heart failure. Baseline heart failure independently predicted mortality (hazard ratio 3.99, 95% confidence interval 2.71– 5.88) and heart failure rehospitalization (hazard ratio 7.29, 95% confidence interval 5.49– 9.66). Radiofrequency catheter ablation was associated with substantial reductions in antiarrhythmic drug use, declining from 58% before the procedure to 31% at 1 year. Medication analysis confirmed dose de-escalation, particularly for mexiletine, propafenone, and amiodarone. However, patients with heart failure exhibited smaller reductions in beta-blockers and Class III agents. Conclusion: This nationwide cohort offers real-world insight into radiofrequency catheter ablation outcomes for frequent premature ventricular complexes, highlighting divergent prognoses between patients with and without heart failure. Radiofrequency catheter ablation was associated with meaningful reductions in antiarrhythmic medication use, underscoring its potential therapeutic and economic benefits. These findings support radiofrequency catheter ablation as a component of long-term management strategies, while future prospective studies are needed to refine candidate selection and timing of intervention.

Background: Despite the increasing use of machine learning (ML) in clinical research, the early stages of data preparation, especially for structured clinical data, often receive limited methodological scrutiny. These datasets typically contain missing values, complex categorical variables, and imbalanced class distributions, all of which complicate downstream model development and interpretation. Objective: This study introduces a structured preprocessing framework designed to address common challenges in medical tabular data and to assess how preprocessing choices affect the stability and portability of predictive models across settings. Methods: We constructed a modular workflow comprising 3 components. First, preprocessing strategies included imputation for missing data, 3 types of categorical encoding (one-hot, frequency, and target), and resampling approaches for class imbalance (Synthetic Minority Over-sampling Technique [SMOTE] and Random Over Sampling Example [ROSE]). Second, 6 classification algorithms were used to evaluate performance patterns, including logistic regression (LGR), decision tree (DT), random forest, XGBoost (XGB), CatBoost (CAT), and light gradient-boosting machine (LightGBM). Third, we assessed cross-dataset portability using 2 datasets with distinct data-generating mechanisms: a registry for patients with end-stage renal disease (ESRD; n=412) and the population-based Behavioral Risk Factor Surveillance System (BRFSS) 2015 survey. For each dataset, we independently cleaned, standardized, encoded, tuned, and evaluated models using the same predefined hyperparameter search space, without cross-dataset feature matching or pooling the area under the ROC curve (AUC) calculations; the complete pipeline was then rerun on BRFSS as an external replication. Results: One-hot encoding in combination with ROSE yielded the most consistent performance improvements in terms of AUC (0.940) and accuracy (0.932), particularly for classifiers sensitive to class distribution. Notably, ROSE enhanced sensitivity without substantially distorting the original data structure. Feature importance rankings also contributed to model interpretability, and performance trends were largely reproducible in cross-context application. Conclusions: Our findings suggest that preprocessing decisions often treated as ancillary play a central role in shaping model outcomes, especially in high-variance clinical datasets. The proposed framework offers a reproducible and adaptable tool for aligning data preparation with the unique demands of health care prediction tasks and may serve as a foundation for future efforts to standardize preprocessing in clinical ML workflows.

BACKGROUND Despite the increasing use of machine learning (ML) in clinical research, the early stages of data preparation—especially for structured clinical data—often receive limited methodological scrutiny. These datasets typically contain missing values, complex categorical variables, and imbalanced class distributions, all of which complicate downstream model development and interpretation. OBJECTIVE This study introduces a structured preprocessing framework designed to address common challenges in medical tabular data and to assess how preprocessing choices affect the stability and transferability of predictive models across settings. METHODS We constructed a modular workflow comprising three components. First, preprocessing strategies included imputation for missing data, three types of categorical encoding (One-Hot, Frequency, Target), and resampling approaches for class imbalance (SMOTE, ROSE). Second, six classification algorithms were used to evaluate performance patterns: Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM. Third, external validation was conducted across two datasets with distinct data-generating mechanisms: an ESRD patient registry (n=412) and the population-based BRFSS 2015 survey. RESULTS One-Hot Encoding in combination with ROSE yielded the most consistent performance improvements in terms of AUC (0.940) and accuracy (0.932), particularly for classifiers sensitive to class distribution. Notably, ROSE enhanced sensitivity without substantially distorting the original data structure. Feature importance rankings also contributed to model interpretability, and performance trends were largely reproducible in external validation. CONCLUSIONS Our findings suggest that preprocessing decisions—often treated as ancillary—play a central role in shaping model outcomes, especially in high-variance clinical datasets. The proposed framework offers a reproducible and adaptable tool for aligning data preparation with the unique demands of healthcare prediction tasks, and may serve as a foundation for future efforts to standardize preprocessing in clinical ML workflows.