Unsupervised Phenotyping using Tensor Factorization
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How can we distill multiple aspects of raw and noisy electronic health record data, such as diagnoses, medications and procedures, to a few concise clinical states without human-annotated labels? In this talk, we present a review of works tackling this challenge through the use of tensor factorization methods. Several aspects of this problem will be discussed such as scalable and efficient computations, interpretability of the results and handling temporally-evolving data.