To date, few programs that integrate traditional practices with evidence-based practices have been developed, implemented, and evaluated with urban American Indians/Alaska Natives (AI/ANs) using a strong research design. The current study recruited u...
Studies in health technology and informatics
31438086
We applied an open source natural language processing (NLP) system "NimbleMiner" to identify clinical notes with mentions of alcohol and substance abuse. NimbleMiner allows users to rapidly discover clinical vocabularies (using word embedding model) ...
BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypoth...
BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.
While the Internet has become a popular and effective strategy for recruiting substance users into research, there is a large risk of recruiting duplicate individuals and Internet bots that pose as humans. Strategies to mitigate these issues are outl...
Journal of medical toxicology : official journal of the American College of Medical Toxicology
31919800
INTRODUCTION: Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. Wastewater-based epidemiology (WBE) is a potential method to quantify community-level trends of opio...
Prevention science : the official journal of the Society for Prevention Research
31960262
Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to...
International journal of medical informatics
32353752
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.
Journal of child psychology and psychiatry, and allied disciplines
32237241
BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.
PURPOSE OF REVIEW: To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice.