  We aim to develop clustering models to obtain behavioral representations from
continuous multimodal mobile sensing data towards relapse prediction tasks. The
identified clusters could represent different routine behavioral trends related
to daily living of patients as well as atypical behavioral trends associated
with impending relapse.
  We used the mobile sensing data obtained in the CrossCheck project for our
analysis. Continuous data from six different mobile sensing-based modalities
(e.g. ambient light, sound/conversation, acceleration etc.) obtained from a
total of 63 schizophrenia patients, each monitored for up to a year, were used
for the clustering models and relapse prediction evaluation. Two clustering
models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were
used to obtain behavioral representations from the mobile sensing data. The
features obtained from the clustering models were used to train and evaluate a
personalized relapse prediction model using Balanced Random Forest. The
personalization was done by identifying optimal features for a given patient
based on a personalization subset consisting of other patients who are of
similar age.
  The clusters identified using the GMM and PAM models were found to represent
different behavioral patterns (such as clusters representing sedentary days,
active but with low communications days, etc.). Significant changes near the
relapse periods were seen in the obtained behavioral representation features
from the clustering models. The clustering model based features, together with
other features characterizing the mobile sensing data, resulted in an F2 score
of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation
setting. This obtained F2 score is significantly higher than a random
classification baseline with an average F2 score of 0.042.

Machine Learning