Harnessing Health Signals with Large-Scale Mobile Sensor Data for Modeling Substance Use Disorder

Personal and population health applications built on top of large-scale mobile sensor data and computing platforms have a great potential to impact the way we diagnose diseases, track, and manage our health. However, the existing sensing mechanisms often fail to accurately capture and infer syndromic signatures that are indicative of anomalies in internal physiological and behavioral processes at an earlier stage. A mobile sensing system that can harness early syndromic signals at an individual or a community level can pave the way to effective just-in-moment intervention, early screening, and prevention.

In this talk, I will present our recent and ongoing research to demonstrate how physiological time series data harnessed from on-body wearable systems can be used for modeling opioid use/administration, affective states including craving, pain, stress and euphoria, and opioid misuse.  I will talk about different approaches (attention based approach and Large Language Model based approach) to fuse multimodal physiological biomarker data, behavioral data, clinical health record data, demographic data as well as symptom data. I will highlight how integration of pharmacological knowledge such as Pharmacokinetics of a specific substance can help neural networks to better generalize and learn opioid related physiological events better than a purely data driven approach. 

Chronic opioid use induces neuroplastic changes in brain circuits, causing predictable changes in different states including heightened stress, increased pain, and intense cravings. The fluctuations or changes in stress, pain and craving states carries telltale signal for opioid misuse risk. In the last part of the talk, I will present how the heart rate variability data from a wearable wristband can be used to predict momentary pain, stress and craving state trajectories with a personalized hierarchical deep learning model, obviating the need for obtrusive ecological momentary assessments throughout the day. We adopt a nonlinear dynamical systems approach with different features including persistence entropy to extract subtle trends from the moment-by-moment fluctuations or changes in pain, stress and craving states. Our analysis reveals a hidden counter-intuitive association between high entropy or lack of predictability (i.e., chaos) in the momentary pain, stress and craving states with the decrease in opioid misuse risk. Leveraging Chaos Theory, the entropy-based nonlinear dynamical features can be used to train a deep learning based approach for accurate opioid misuse risk assessment.