  Background: 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 breast, prostate,
colorectal, or lung cancer in the Northwestern Medicine Enterprise Data
Warehouse from 2000 to 2015. In our experiments, we examined using a supervised
term in the factorization algorithm, filtering tensor co-occurrences by medical
indication, and incorporating additional social determinants of health (SDOH)
covariates in the factorization process. We evaluated the resulting
computational phenotypes qualitatively and by assessing their ability to
predict five-year mortality using the area under the curve (AUC) statistic.
Results: Filtering by medical indication led to more concise and interpretable
phenotypes. Mortality prediction performance (AUC) varied under the different
experimental conditions and by cancer type (breast: 0.623 - 0.694, prostate:
0.603 - 0.750, colorectal: 0.523 - 0.641, and lung: 0.517 - 0.623). Generally,
prediction performance improved with the use of a supervised term and the
incorporation of SDOH covariates. Conclusion: Constrained tensor factorization,
applied to sparse EHR data of patients with cancer, can discover computational
phenotypes predictive of five-year mortality. The incorporation of SDOH
variables into the factorization algorithm is an easy-to-implement and
effective way to improve prediction performance.
