  Models have been proposed to extract temporal patterns from longitudinal
electronic health records (EHR) for clinical predictive models. However, the
common relations among patients (e.g., receiving the same medical treatments)
were rarely considered. In this paper, we propose to learn patient similarity
features as phenotypes from the aggregated patient-medical service matrix using
non-negative matrix factorization. On real-world medical claim data, we show
that the learned phenotypes are coherent within each group, and also
explanatory and indicative of targeted diseases. We conducted experiments to
predict the diagnoses for Chronic Lymphocytic Leukemia (CLL) patients. Results
show that the phenotype-based similarity features can improve prediction over
multiple baselines, including logistic regression, random forest, convolutional
neural network, and more.
