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 …

Homology Continuity is a fundamental property of the nature, but few of the traditional
pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to …

To improve the scientificity and reliability of medical behavior analysis, this paper combines
machine learning and wireless sensor technology to construct an intelligent data mining …

Missing data are common in longitudinal observational and randomized controlled trials in
smart health studies. Multiple-imputation based fuzzy clustering is an emerging non …

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity
of treatment effects, unsupervised learning methods have been widely applied. However …

Disentangling patients' behavioral variations is a critical step for better understanding an
intervention's effects on individual outcomes. Missing data commonly exist in longitudinal …

Abstract Background Korean Americans are known for a high smoking prevalence within the
Asian American population. This study examined the effects of acculturation and depression …

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 …