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Sepsis continues to be a major contributor to morbidity and mortality among hospitalized patients globally, especially within intensive care and emergency departments, where rapid recognition is essential for improving survival through timely treatment. In recent years, machine learning approaches have gained attention for their ability to predict sepsis onset using routinely collected electronic health record data. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes evidence from studies published between 2017 and 2025, focusing on model architectures, feature selection and engineering strategies, prediction time horizons, and validation methodologies. Searches across major biomedical and informatics databases identified 67 eligible studies. The included literature shows that logistic regression, ensemble tree-based algorithms, and deep learning models are most frequently applied for sepsis prediction tasks. However, the majority of studies rely on retrospective datasets with internal validation, while only a limited number incorporate prospective or real-world validation frameworks. Overall, although reported model performance is often strong in retrospective analyses, a consistent decline in accuracy is observed when models are evaluated in real clinical environments. These findings highlight that prospective validation and improved generalizability are still underdeveloped areas, underscoring the need for future research to emphasize real-time deployment and robust external validation before clinical integration.

Public health emergencies reveal critical weaknesses in healthcare supply chains, especially when PPE demand outpaces procurement and distribution capacity, making predictive analytics an important tool for forecasting demand and improving allocation during crises. This systematic review evaluates predictive analytics models for PPE demand forecasting and distribution optimization during public health emergencies, focusing on model types, data sources, validation approaches, performance metrics, equity considerations, and implementation readiness. Following PRISMA 2020 guidelines, searches were conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2017 and 2025, yielding 2,847 records, of which 35 met inclusion criteria. Included studies comprised time series and statistical models (34%), machine learning and hybrid approaches (29%), optimization methods (26%), and simulation or digital twin frameworks (11%), with limited evidence of real-world deployment. Overall, findings indicate that predictive analytics can enhance PPE supply chain resilience by improving demand forecasting, allocation decisions, and scenario testing, but widespread adoption is limited by poor data interoperability, insufficient prospective validation, weak equity integration, and limited operational integration into healthcare decision systems.

Federated and decentralized machine learning offer the potential to extract valuable healthcare insights from siloed data without requiring the centralization of sensitive patient records, addressing long-standing privacy and governance challenges. This critical review assesses federated learning in healthcare through three lenses: privacy-preserving technologies, incentive mechanisms, and regulatory compliance frameworks. It examines whether the claims in existing literature are substantiated by real-world evidence from healthcare settings. The review reveals considerable enthusiasm for federated learning but identifies gaps, including incomplete implementation of privacy technologies, theoretical incentive mechanisms, and regulatory compliance often assumed but not validated. Additionally, real-world deployments are limited in scale and duration. The review concludes that the gap between federated learning's theoretical potential and clinical application remains significant, with overstated privacy claims and a lack of established frameworks for incentives and compliance.