Addictions

Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery...

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more th...

Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend asses...

Accelerated Aging of the Amygdala in Alcohol Use Disorders: Relevance to the Dark Side of Addiction.

Here we assessed changes in subcortical volumes in alcohol use disorder (AUD). A simple morphometry-...

Big data and predictive modelling for the opioid crisis: existing research and future potential.

A need exists to accurately estimate overdose risk and improve understanding of how to deliver treat...

Big data, artificial intelligence, and the opioid crisis.

Towards Equitable AI Interventions for People Who Use Drugs: Key Areas That Require Ethical Investment.

There has been growing investment in artificial intelligence (AI) interventions to combat the opioid...

Recombinant human thyrotropin thyroid hormone withdrawal in differentiated thyroid carcinoma follow-up: a single center experience.

INTRODUCTION: Our goal was to evaluate and compare the diagnostic utility of thyroid hormone withdra...

Characterizing Opioid Overdoses Using Emergency Medical Services Data : A Case Definition Algorithm Enhanced by Machine Learning.

OBJECTIVES: Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but ...

Development of a machine learning algorithm for early detection of opioid use disorder.

BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosi...

Multimodal Automatic Coding of Client Behavior in Motivational Interviewing.

Motivational Interviewing (MI) is defined as a collaborative conversation style that evokes the clie...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - ...

A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors.

Determining the drug-target residence time (RT) is of major interest in drug discovery given that th...

Improved Classification of Medical Data Using Meta-Best Feature Selection.

Feature selection provides a useful method for reducing the size of large data sets while maintainin...

Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

BACKGROUND: Machine-learning methods such as the Bayesian belief network, random forest, gradient bo...

An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.

BACKGROUND: Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment o...

Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children.

Machine learning provides a method of identifying factors that discriminate between substance users ...

Identifying opioid misuse among adolescents: Is the answer hiding in plain sight?

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.

The current study utilized a random forest regression analysis to predict post-experiment fatigue in...

Regulating Child Sex Robots: Restriction or Experimentation?

In July 2014, the roboticist Ronald Arkin suggested that child sex robots could be used to treat tho...

Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data.

We applied an open source natural language processing (NLP) system "NimbleMiner" to identify clinica...