  This paper presents a new method, which we call SUSTain, that extends
real-valued matrix and tensor factorizations to data where values are integers.
Such data are common when the values correspond to event counts or ordinal
measures. The conventional approach is to treat integer data as real, and then
apply real-valued factorizations. However, doing so fails to preserve important
characteristics of the original data, thereby making it hard to interpret the
results. Instead, our approach extracts factor values from integer datasets as
scores that are constrained to take values from a small integer set. These
scores are easy to interpret: a score of zero indicates no feature contribution
and higher scores indicate distinct levels of feature importance.
  At its core, SUSTain relies on: a) a problem partitioning into
integer-constrained subproblems, so that they can be optimally solved in an
efficient manner; and b) organizing the order of the subproblems' solution, to
promote reuse of shared intermediate results. We propose two variants,
SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively.
We evaluate SUSTain against several state-of-the-art baselines on both
synthetic and real Electronic Health Record (EHR) datasets. Comparing to those
baselines, SUSTain shows either significantly better fit or orders of magnitude
speedups that achieve a comparable fit (up to 425X faster). We apply SUSTain to
EHR datasets to extract patient phenotypes (i.e., clinically meaningful patient
clusters). Furthermore, 87% of them were validated as clinically meaningful
phenotypes related to heart failure by a cardiologist.
