  This work proposes a new algorithm for automated and simultaneous phenotyping
of multiple co-occurring medical conditions, also referred as comorbidities,
using clinical notes from the electronic health records (EHRs). A basic latent
factor estimation technique of non-negative matrix factorization (NMF) is
augmented with domain specific constraints to obtain sparse latent factors that
are anchored to a fixed set of chronic conditions. The proposed anchoring
mechanism ensures a one-to-one identifiable and interpretable mapping between
the latent factors and the target comorbidities. Qualitative assessment of the
empirical results by clinical experts suggests that the proposed model learns
clinically interpretable phenotypes while being predictive of 30 day mortality.
The proposed method can be readily adapted to any non-negative EHR data across
various healthcare institutions.
