Navigating Critical Care with Machine Learning: Advances in Personalized Emergency Department Practices
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 14
Special Issue Editor
2. Departmental Faculty of Medicine, St. Camillus University of Medicine and Health Sciences, 00131 Rome, Italy
Interests: internal medicine; acute complex patients management; emergencies
Special Issue Information
Dear Colleagues,
Machine learning (ML) is revolutionizing critical care by enabling personalized and data-driven decision-making in emergency departments (EDs). By analyzing vast amounts of patient data, ML algorithms can predict disease progression, identify high-risk patients, and optimize treatment plans in real time. These advancements enhance diagnostic accuracy, reduce response times, and improve patient outcomes. Key applications include early detection of sepsis, stroke, and cardiac events, as well as resource allocation and workflow optimization. However, challenges such as data privacy, model interpretability, and integration into clinical workflows remain. As ML continues to evolve, its potential to transform ED practices into more efficient, patient-centered systems is immense, paving the way for a new era of precision emergency medicine.
This Special Issue aims to explore the cutting-edge applications of machine learning (ML) in critical care in the emergency department (ED) and demonstrate its potential and challenges in personalized medical practice. By bringing together the latest research results and clinical practice cases, this Special Issue is committed to promoting the deep integration of ML technology in emergency medicine, improving diagnostic efficiency, optimizing treatment plans, and improving patient prognosis. At the same time, this Special Issue will also explore key issues such as data privacy, model interpretability, and clinical implementation to provide directions for future research.
Dr. Filomena Pietrantonio
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- emergency department
- critical care
- personalized medicine
- predictive modeling
- real-time decision support
- clinical disease
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