  In the healthcare sector, a consciousness surrounding data privacy and
corresponding data protection regulations, as well as heterogeneous and
non-harmonized data, pose huge challenges to large-scale data analysis.
Moreover, clinical data often involves partially overlapping features, as some
observations may be missing due to various reasons, such as differences in
procedures, diagnostic tests, or other recorded patient history information
across hospitals or institutes. To address the challenges posed by partially
overlapping features and incomplete data in clinical datasets, a comprehensive
approach is required. Particularly in the domain of medical data, promising
outcomes are achieved by federated random forests whenever features align.
However, for most standard algorithms, like random forest, it is essential that
all data sets have identical parameters. Therefore, in this work the concept of
federated random forest is adapted to a setting with partially overlapping
features. Moreover, our research assesses the effectiveness of the newly
developed federated random forest models for partially overlapping clinical
data. For aggregating the federated, globally optimized model, only features
available locally at each site can be used. We tackled two issues in
federation: (i) the quantity of involved parties, (ii) the varying overlap of
features. This evaluation was conducted across three clinical datasets. The
federated random forest model even in cases where only a subset of features
overlaps consistently demonstrates superior performance compared to its local
counterpart. This holds true across various scenarios, including datasets with
imbalanced classes. Consequently, federated random forests for partially
overlapped data offer a promising solution to transcend barriers in
collaborative research and corporate cooperation.
