  A large percentage of medical information is in unstructured text format in
electronic medical record systems. Manual extraction of information from
clinical notes is extremely time consuming. Natural language processing has
been widely used in recent years for automatic information extraction from
medical texts. However, algorithms trained on data from a single healthcare
provider are not generalizable and error-prone due to the heterogeneity and
uniqueness of medical documents. We develop a two-stage federated natural
language processing method that enables utilization of clinical notes from
different hospitals or clinics without moving the data, and demonstrate its
performance using obesity and comorbities phenotyping as medical task. This
approach not only improves the quality of a specific clinical task but also
facilitates knowledge progression in the whole healthcare system, which is an
essential part of learning health system. To the best of our knowledge, this is
the first application of federated machine learning in clinical NLP.
