Patient Level

Patient-level analysis in healthcare focuses on identifying meaningful subgroups of patients based on diverse data sources, including clinical records, imaging, and digital interactions, to improve personalized medicine and clinical decision-making. Current research emphasizes developing robust and privacy-preserving clustering algorithms, such as federated learning and novel neural network architectures (e.g., recurrent neural networks, ResNets), to analyze data from multiple institutions while protecting patient confidentiality. These advancements enable more accurate patient stratification for improved treatment strategies, clinical trial design, and the development of AI-driven tools for diagnosis and prognosis.