About 20% of individuals with attention deficit hyperactivity disorder (ADHD) are first
diagnosed during adolescence. While preclinical experiments suggest that adolescent …

Understanding patient trajectories and identifying patterns in episodes of care is critical for
effective healthcare decision-making. We present a patient timeline visualization using …

People nowadays spend a significant amount of time on social media such as Twitter,
Facebook, and Instagram. As a result, social media data capture rich human behavioral …

With the exponential growth of clinical data which are longitudinal, sparse and
heterogeneous, deep learning methods are receiving increasingly attention for predictive …

Type 2 diabetes (T2D) is a major public health problem, and opportunistic screening to
detect T2D at an early stage can help initiate interventions that delay or prevent the disease …

Risk prediction estimates the probability of future adverse outcomes for high-risk individuals
to enable early intervention. Among all machine learning models, deep learning models can …

Currently, around 7 to 12 percent of world population of children has attention deficit
hyperactivity disorder (ADHD), especially 12 percent in children school age and about 6.5 …

Machine learning models are increasingly utilized to predict ADHD based on various
features and behavioural indicators. These models analyze demographic, clinical, and …

Prediction of alcohol use disorder (AUD) may reduce the number of deaths caused by
alcohol-related diseases. However, prediction of AUD based on patients' historical clinical …

Alzheimer's disease (AD) is one of the leading causes of death in the United States,
especially among the elderly. Recent studies have shown how hypertension is related to …