2016/08/02 by Shalmali Joshi, Joshi, Shalmali, Suriya Gunasekar +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #FOS: Computer and information sciences #Face and Expression Recognition #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1608.00704
openalex publication_date 2016/08/02 · openalex created_date 2016/08/23 · openalex updated_date 2026/07/28
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.