Late fusion of machine learning models using passively captured interpersonal social interactions and motion from smartphones predicts decompensation in heart failure

TL;DR

This study demonstrates that combining passive smartphone data with patient-reported outcomes using late fusion machine learning models can predict heart failure decompensation events with an AUC of 0.80, potentially enabling early intervention.

Contribution

It introduces a novel late fusion approach integrating passive smartphone data and patient reports to predict heart failure decompensation.

Findings

Late fusion models achieved an AUC of 0.80 in predicting decompensation.

Passive smartphone data combined with patient reports can reflect physiological changes.

The approach may enable proactive management of heart failure.

Abstract

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.