[图书][B] Machine Learning for Social and Behavioral Research
Today's social and behavioral researchers increasingly need to know:" What do I do with all
this data?" This book provides the skills needed to analyze and report large, complex data …
this data?" This book provides the skills needed to analyze and report large, complex data …
From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools
Advances in artificial intelligence and machine learning have fueled growing interest in the
application of predictive analytics to identify high‐risk suicidal patients. Such application will …
application of predictive analytics to identify high‐risk suicidal patients. Such application will …
Concurrent and prospective associations between fitbit wearable‐derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms
Background Adolescence is characterized by alterations in biobehavioral functioning, during
which individuals are at heightened risk for onset of psychopathology, particularly …
which individuals are at heightened risk for onset of psychopathology, particularly …
Predicting treatment response using machine learning: A registered report
Objective Previous research on psychotherapy treatment response has mainly focused on
outpatients or clinical trial data which may have low ecological validity regarding naturalistic …
outpatients or clinical trial data which may have low ecological validity regarding naturalistic …
On the selection of item scores or composite scores for clinical prediction
K McClure, BA Ammerman… - Multivariate Behavioral …, 2024 - Taylor & Francis
Recent shifts to prioritize prediction, rather than explanation, in psychological science have
increased applications of predictive modeling methods. However, composite predictors …
increased applications of predictive modeling methods. However, composite predictors …
A critique of using the labels confirmatory and exploratory in modern psychological research
R Jacobucci - Frontiers in Psychology, 2022 - frontiersin.org
Psychological science is experiencing a rise in the application of complex statistical models
and, simultaneously, a renewed focus on applying research in a confirmatory manner. This …
and, simultaneously, a renewed focus on applying research in a confirmatory manner. This …
Cross-Sectional and Longitudinal Correlates of Interrupted and Aborted Suicide Attempts Among US Active Duty Service Members Seeking Treatment for Suicidal …
This study examined suicide attempts (SAs), interrupted SAs, and aborted SAs and their
cross-sectional and longitudinal associations with suicide-related correlates among high …
cross-sectional and longitudinal associations with suicide-related correlates among high …
Personalizing suicidology.
CJR Sewall, AGC Wright - 2021 - psycnet.apa.org
Since the dawn of modern statistics, suicidologists have been employing standard group-
level analyses as the predominant quantitative approach to understanding and predicting …
level analyses as the predominant quantitative approach to understanding and predicting …
Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide
Background Strategies to detect the presence of suicidal ideation (SI) or characteristics of
ideation that indicate marked suicide risk are critically needed to guide interventions and …
ideation that indicate marked suicide risk are critically needed to guide interventions and …
Prediction models of suicide and non‐fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish …
SD Nielsen, RHB Christensen… - Acta Psychiatrica …, 2023 - Wiley Online Library
Introduction To develop machine learning models capable of predicting suicide and non‐
fatal suicide attempt as separate outcomes in the first 30 days after discharge from a …
fatal suicide attempt as separate outcomes in the first 30 days after discharge from a …