Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.

In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C in health care, including its present stage, potential opportunities, and associated challenges.