AI in Medicine

The increasing challenges of healthcare systems demand innovative approaches to resource optimization, particularly for hospitals operating under economic and operational constraints. This study investigates the organizational and managerial factors influencing scale efficiency in 127 Italian hospitals, leveraging advanced machine learning (ML) techniques to identify key determinants of efficiency. A multi-level framework was developed, integrating Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), and K-Means clustering to assess the interplay between energy costs, staff composition, and medical equipment across hospital levels. To enhance predictive capabilities, a classification model based on K-Nearest Neighbors (K-NN) was implemented, demonstrating high performance in distinguishing efficiency classes and confirming the importance of targeted resource management strategies. Additionally, the use of LIME (Local Interpretable Model-agnostic Explanations) provided actionable insights into the contribution of individual variables, enabling a deeper understanding of their impact on operational efficiency. In conclusion, this research highlights the importance of an integrated approach to support decision-makers in managing hospital resources, offering innovative tools to optimize efficiency and ensure the economic and operational sustainability of the healthcare system. Full article

Background: Mapping anti–HLA class I cross-reactivity from single-antigen bead (SAB) mean fluorescence intensity (MFI) data supports donor selection. However, interpretation is complicated by analytical choices and assay variability. Methods: A total of 4327 SAB assays were analyzed (antigen × test matrix) using an interpretable, distance-based workflow. Antigen profiles were z-scored across tests. Multidimensional scaling (MDS) was used for visualization and hierarchical clustering analysis (HCA) for grouping, and complemented these with a common PCA space for model selection (K-Means via Silhouette; Gaussian Mixture Models via BIC), agglomerative (Ward and average-link on 1–correlation), spectral clustering on correlation-derived affinities, and a graph approach (k-NN ∪ minimum-spanning-tree with modularity-based communities). Non-linear embeddings (t-SNE/UMAP) and density-based HDBSCAN were used only for visual analytics, not for primary inference. Results: The pipeline revealed coherent reactivity neighborhoods that partially overlapped known cross-reactive antigen groups (CREGs) and eplet-based expectations while also highlighting less-documented relationships. Robustness was confirmed through bootstrap resampling, graph modularity, and consensus clustering across methods. Conclusions: This unified, auditable workflow converts descriptive maps into method-robust summaries of antibody reactivity and cross-reactivity. While exploratory and performed on a single dataset without linked outcomes, the approach provides a reproducible structure for comparing cohorts and prioritizing hypotheses that could be prospectively validated for clinical decision support in transplantation. Full article