  Clinical records frequently include assessments of the characteristics of
patients, which may include the completion of various questionnaires. These
questionnaires provide a variety of perspectives on a patient's current state
of well-being. Not only is it critical to capture the heterogeneity given by
these perspectives, but there is also a growing demand for developing
cost-effective technologies for clinical phenotyping. Filling out many
questionnaires may be a strain for the patients and therefore costly. In this
work, we propose COBALT -- a cost-based layer selector model for detecting
phenotypes using a community detection approach. Our goal is to minimize the
number of features used to build these phenotypes while preserving its quality.
We test our model using questionnaire data from chronic tinnitus patients and
represent the data in a multi-layer network structure. The model is then
evaluated by predicting post-treatment data using baseline features (age,
gender, and pre-treatment data) as well as the identified phenotypes as a
feature. For some post-treatment variables, predictors using phenotypes from
COBALT as features outperformed those using phenotypes detected by traditional
clustering methods. Moreover, using phenotype data to predict post-treatment
data proved beneficial in comparison with predictors that were solely trained
with baseline features.
