Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

Perhaps no other topic in risk analysis is more difficult, more controversial, or more important
to risk management policy analysts and decision-makers than how to draw valid, correctly …

Objective: The present study examined the longitudinal association between personality
traits and sleep quality in 4 samples of middle-aged and older adults. Method: Participants …

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a
well-known model for causal discovery. This paper describes an open-source Python …

Importance Sleep is critical to a person's physical and mental health, but there are few
studies systematically assessing risk factors for sleep disorders. Objective The objective of …

In many empirical sciences, the causal mechanisms underlying various phenomena need to
be studied. Structural equation modeling is a general framework used for multivariate …

Objective We introduced methods for solving causal direction of dependence between
variables observed in pre-and post-psychotherapy assessments, showing how to apply …

This study was conducted to investigate the prevalence and associated factors of
depression in medical students. This cross-sectional study investigated the prevalence and …

Compassion is known to predict prosocial behavior and moral judgments related to harm.
Despite the centrality of compassion to social life, factors predicting adulthood compassion …

The F-measure, which has originally been introduced in information retrieval, is nowadays
routinely used as a performance metric for problems such as binary classification, multi-label …