activity
20242026
most citedFrom Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering

Soorya Ram Shimgekar, Michelle Hu, Dorisa Shehi +10

Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload. This is especially pronoun…

cs.IR2026

Analysis of Autonomic Regulation in Cancer Survivors During Daily Physical Activity: A Real-World Wearable ECG Study

Sajad Farrokhi, Lerick Sequeira, Shanna L. Burke +2

This study investigates heart rate (HR) and heart rate variability (HRV) responses to physical activity in breast cancer survivors using wearable electrocardiogram (ECG) data colle…

cs.LG2026

STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments

Md Mezbahul Islam, John Michael Templeton, Christian Poellabauer +1

Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essential for clinical monitoring and…

cs.LG2026

SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making

Md Mezbahul Islam, John Michael Templeton, Masrur Sobhan +2

Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this disease early is challenging be…

cs.AI20261 cited

From Wearables to Warnings: Predicting Pain Spikes in Patients with Opioid Use Disorder

Abhay Goyal, Navin Kumar, Kimberly DiMeola +7

Chronic pain (CP) and opioid use disorder (OUD) are common and interrelated chronic medical conditions. Currently, there is a paucity of evidence-based integrated treatments for CP…

cs.NI2025

Prediction of the Received Power of Low-Power Networks Using Inertial Sensors

Waltenegus Dargie, Christian Poellabauer, Abiy Tasissa

Low-power and cost-effective IoT sensing nodes enable scalable monitoring of different environments. Some of these environments impose rough and extreme operating conditions, requi…