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Sepsis is a leading cause of ICU mortality, and early detection is critical for improving patient outcomes. However, existing machine learning models often rely on hourly aggregated data, limiting their ability to capture rapid physiological changes, and frequently lack interpretability, reducing clinical trust and usability. This paper proposes a conceptual framework that integrates Temporal Convolutional Networks (TCNs) with an attention mechanism to analyze high-frequency, minute-level vital sign data for early sepsis prediction. The architecture includes a data input layer, a TCN-based feature extractor with causal dilated convolutions and residual connections, an attention module for identifying clinically relevant time points and variables, and a prediction head that estimates the risk of sepsis within a 6-hour horizon. The proposed approach enables efficient parallel processing, improved temporal sensitivity, and enhanced interpretability compared to recurrent models. While offering advantages in real-time prediction and explainability, challenges remain in handling missing data, ensuring generalizability across ICUs, and minimizing false alarms for clinical deployment.

Acute kidney injury (AKI) is a common and serious condition in critical care, making early prediction essential for timely intervention, reduced mortality, and lower healthcare costs. Machine learning methods using electronic health records have shown promise in identifying at-risk patients, but their performance is often limited by reliance on single-institution datasets and poor generalizability across populations. Privacy regulations such as HIPAA and GDPR further restrict cross-hospital data sharing, hindering the development of more robust models.To address these challenges, this study proposes a federated learning–based framework for AKI prediction, enabling multiple hospitals to collaboratively train models without exchanging raw patient data. Each institution acts as a local client that trains on its own data and shares only model updates, which are aggregated into a global model. The framework incorporates standardized feature processing, secure aggregation, and communication-efficient strategies to ensure scalability across heterogeneous healthcare environments.This privacy-preserving approach improves model generalization by leveraging diverse multi-institutional data while maintaining regulatory compliance. Although it introduces challenges such as communication overhead and convergence complexity, these are mitigated through optimized aggregation methods. Overall, the proposed framework enhances predictive performance, supports clinical decision-making, and offers a scalable foundation for future privacy-aware healthcare AI systems in AKI management.

Polypharmacy, defined as the concurrent use of five or more medications, is highly prevalent among older adults and patients with multiple chronic conditions and is associated with an increased risk of drug–drug interactions (DDIs), leading to adverse drug events, hospitalizations, and higher healthcare costs. Existing DDI databases are often incomplete and fail to capture higher-order interactions, while many machine learning approaches overlook temporal prescription patterns and molecular structure information, limiting their effectiveness in real-world clinical settings. To address these limitations, this study proposes a graph neural network (GNN)-based framework that integrates prescription sequence data with molecular representations to improve DDI prediction. The model constructs a unified graph where drug nodes encode both known interactions and learned similarities, while a prescription sequence encoder captures temporal co-prescribing patterns and a molecular encoder processes SMILES-based structures. These multimodal representations are fused within a patient–drug interaction graph and refined using GNN layers with attention mechanisms to enhance interpretability. By combining longitudinal clinical data with chemical structure information, the framework enables more accurate, context-aware, and patient-specific prediction of DDIs, supports the identification of novel interactions, and improves risk stratification in polypharmacy settings, offering a scalable and interpretable foundation for future clinical decision support systems.

Sepsis prediction models in intensive care units often degrade over time due to changes in clinical practice, patient populations, and data recording processes, a phenomenon known as model drift that can compromise patient safety. Traditional federated learning approaches are not well-suited to these evolving conditions, as they assume static data distributions and typically require costly retraining that risks forgetting previously learned knowledge, while also being constrained by privacy limitations that prevent central data pooling. To address these challenges, this paper proposes a federated continual learning framework that enables ongoing, privacy-preserving model adaptation across multiple hospitals without catastrophic forgetting. The framework integrates local continual learning methods (such as elastic weight consolidation or memory replay) with federated aggregation and importance-weighted parameter updates to support continuous learning from new clinical data while preserving prior knowledge. This design allows each institution to adapt models to local data shifts while collaboratively improving a shared global model without sharing patient-level data. Overall, the proposed approach offers a scalable solution for maintaining robust, adaptive sepsis prediction systems in dynamic healthcare environments, reducing the need for repeated full retraining and supporting long-term clinical deployment.

Pancreatic cancer is highly lethal, and surgical resection is the only curative option. Preoperative assessment using contrast-enhanced CT is essential for determining tumor resectability based on involvement of key vessels such as the superior mesenteric artery, celiac trunk, and portal vein. Accurate pancreatic tumor segmentation is difficult due to unclear boundaries, low contrast with surrounding tissue, and proximity to major vessels. Manual segmentation is slow, subjective, and inconsistent, especially in borderline cases, while tumor-associated fibrosis further obscures lesion margins. We propose a deep learning-based framework using an attention-enhanced U-Net with multi-scale feature fusion and deep supervision for tumor segmentation and resectability assessment. The model incorporates attention gates, atrous spatial pyramid pooling, and auxiliary losses at multiple decoder levels to improve feature learning and gradient flow. A pre-trained encoder extracts hierarchical features refined by attention mechanisms in skip connections. A multi-scale decoder reconstructs segmentation maps, supported by deep supervision at different resolutions. A parallel branch models tumor–vessel spatial relationships using distance maps to improve resectability classification. This framework enables automated pancreatic tumor segmentation and resectability evaluation from CT scans, improving accuracy, interpretability, and clinical utility. Validation on datasets such as Pancreas-CT and Medical Segmentation Decathlon is recommended.