  Detailed mobile sensing data from phones, watches, and fitness trackers offer
an unparalleled opportunity to quantify and act upon previously unmeasurable
behavioral changes in order to improve individual health and accelerate
responses to emerging diseases. Unlike in natural language processing and
computer vision, deep representation learning has yet to broadly impact this
domain, in which the vast majority of research and clinical applications still
rely on manually defined features and boosted tree models or even forgo
predictive modeling altogether due to insufficient accuracy. This is due to
unique challenges in the behavioral health domain, including very small
datasets (~10^1 participants), which frequently contain missing data, consist
of long time series with critical long-range dependencies (length>10^4), and
extreme class imbalances (>10^3:1). Here, we introduce a neural architecture
for multivariate time series classification designed to address these unique
domain challenges. Our proposed behavioral representation learning approach
combines novel tasks for self-supervised pretraining and transfer learning to
address data scarcity, and captures long-range dependencies across long-history
time series through transformer self-attention following convolutional neural
network-based dimensionality reduction. We propose an evaluation framework
aimed at reflecting expected real-world performance in plausible deployment
scenarios. Concretely, we demonstrate (1) performance improvements over
baselines of up to 0.15 ROC AUC across five prediction tasks, (2) transfer
learning-induced performance improvements of 16% PR AUC in small data
scenarios, and (3) the potential of transfer learning in novel disease
scenarios through an exploratory case study of zero-shot COVID-19 prediction in
an independent data set. Finally, we discuss potential implications for medical
surveillance testing.
