AIMC Topic: Substance-Related Disorders
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Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.
Mathematical biosciences and engineering : MBE
Aug 20, 2021
Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signa...
Machine learning-based outcome prediction and novel hypotheses generation for substance use disorder treatment.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a nove...
Machine-learning approaches to substance-abuse research: emerging trends and their implications.
Current opinion in psychiatry
Jul 1, 2020
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.
Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children.
Prevention science : the official journal of the Society for Prevention Research
Feb 1, 2020
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...
Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks.
Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data.
Studies in health technology and informatics
Aug 21, 2019
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) ...
Factors Associated with HIV Viral Suppression Among Transgender Women in Lima, Peru.
LGBT health
Jan 1, 2018
PURPOSE: Globally, transgender women (TGW) experience a high burden of adverse health outcomes, including a high prevalence of HIV and sexually transmitted infections (STIs) as well as psychiatric disorders and substance use disorders. To address gap...
Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.
AIDS and behavior
Feb 1, 2017
HIV testing is the foundation for consolidated HIV treatment and prevention. In this study, we aim to discover the most relevant variables for predicting HIV testing uptake among substance users in substance use disorder treatment programs by applyin...
Development of New Diagnostic Techniques - Machine Learning.
Advances in experimental medicine and biology
Jan 1, 2017
Traditional diagnoses on addiction reply on the patients' self-reports, which are easy to be dampened by false memory or malingering. Machine learning (ML) is a data-driven procedure that learns algorithms from training data and makes predictions. It...
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