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Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing
Angielski
186 b
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This Special Issue presents recent progress in applying machine learning and deep learning techniques to healthcare data analysis and clinical decision support. Modern healthcare systems generate extensive and diverse data sources, including physiological signals, medical imaging, electronic health records, wearable sensor outputs, and multimodal patient monitoring streams. Deriving reliable clinical insights from these data demands approaches that are robust, interpretable, and efficient for real-time or resource-constrained environments. The contributions in this collection demonstrate innovative models and practical methodologies for addressing key challenges such as the detection of Parkinson's disease, prediction of Alzheimer's disease progression, forecasting clinical outcomes in microsurgical clipping of cerebral aneurysms, prediction of lumbar disc herniation, and the development of medical image-based decision support systems. Overall, the Special Issue highlights methods that improve diagnostic accuracy, streamline data processing workflows, and support integration into clinical and point-of-care practice.
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