Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 38370129.
- Also identified by DOI 10.1016/j.patter.2023.100913 and PMC identifier 10873158.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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<sup>4</sup>) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C<sup>4</sup> approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C<sup>4</sup> in health care, including its present stage, potential opportunities, and associated challenges.