The use of medical data for machine learning, including unsupervised methods, such as
clustering, is often restricted by privacy regulations, such as the health insurance portability …

Wearable sensors have gathered tremendous interest for a plethora of applications, yet
there is a void of robust and accurate wearable systems for reliable drug monitoring …

INTRODUCTION We compared smoking behaviors, past quit attempts, readiness to quit and
beliefs about quitting among current cigarette smokers with probable anxiety or depression …

Introduction: Tobacco use is a preventable cause of death among ethnic minorities. Chinese
Americans have high smoking rates and underutilize evidence-based cessation therapies …

Clustering methods become increasingly important in analyzing heterogeneity of treatment
effects, especially in longitudinal behavioral intervention studies. Methods such as K-means …

Longitudinal observational and randomized controlled trials (RCT) are widely applied in
biomedical behavioral studies and increasingly implemented in smart health systems. These …

Fuzzy-logic based algorithms have been applied in learning longitudinal behavioral
intervention data. This paper proposes a modified generalized network-based neuro-fuzzy …

Intelligent behavioral trajectory pattern recognition is a specific area of artificial intelligence,
defined as automated statistical machine learning and visualization of structures of …

Longitudinal data, a common data type in the biomedical area, refer to the data where each
participant has repeatedly measured values on the same variable at two or more time points …

La evaluación de riesgos psicosociales ha desempeñado un papel dominante para
garantizar el bienestar y la salud de las personas. No obstante, mecanismos como …