Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources. (arXiv:2305.06217v1 [cs.LG])

Machine learning (ML) in healthcare presents numerous opportunities for
enhancing patient care, population health, and healthcare providers' workflows.
However, the real-world clinical and cost benefits remain limited due to
challenges in data privacy, heterogeneous data sources, and the inability to
fully leverage multiple data modalities. In this perspective paper, we
introduce "patchwork learning" (PL), a novel paradigm that addresses these
limitations by integrating information from disparate datasets composed of
different data modalities (e.g., clinical free-text, medical images, omics) and
distributed …

analysis benefits biomedical care challenges cost data data privacy data sources health healthcare healthcare providers machine machine learning opportunities paradigm patchwork patient care privacy workflows world