  Objective: Worldwide, heart failure (HF) is a major cause of morbidity and
mortality and one of the leading causes of hospitalization. Early detection of
HF symptoms and pro-active management may reduce adverse events. Approach:
Twenty-eight participants were monitored using a smartphone app after discharge
from hospitals, and each clinical event during the enrollment (N=110 clinical
events) was recorded. Motion, social, location, and clinical survey data
collected via the smartphone-based monitoring system were used to develop and
validate an algorithm for predicting or classifying HF decompensation events
(hospitalizations or clinic visit) versus clinic monitoring visits in which
they were determined to be compensated or stable. Models based on single
modality as well as early and late fusion approaches combining patient-reported
outcomes and passive smartphone data were evaluated. Results: The highest AUCPr
for classifying decompensation with a late fusion approach was 0.80 using leave
one subject out cross-validation. Significance: Passively collected data from
smartphones, especially when combined with weekly patient-reported outcomes,
may reflect behavioral and physiological changes due to HF and thus could
enable prediction of HF decompensation.

Signal Processing;Machine Learning