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Constrained tensor factorization for computational phenotyping and mortality prediction in patients with cancer
[article]
2021
arXiv
pre-print
The increasing adoption of electronic health records (EHR) across the US has created troves of computable data, to which machine learning methods have been applied to extract useful insights. EHR data, represented as a three-dimensional analogue of a matrix (tensor), is decomposed into two-dimensional factors that can be interpreted as computational phenotypes. Methods: We apply constrained tensor factorization to derive computational phenotypes and predict mortality in cohorts of patients with
arXiv:2112.12933v1
fatcat:luwl3oibpjffpcmpnymny5yxwm