Federated unsupervised random forest for privacy-preserving patient stratification.

ABSTRACT:

Motivation

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing.

Re

SUBMITTER: Pfeifer B 

PROVIDER: S-EPMC11373406 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature