Addictions

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.

OBJECTIVE: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD.

Smartphones dependency risk analysis using machine-learning predictive models.

Recent technological advances have changed how people interact, run businesses, learn, and use their free time. The advantages and facilities provided by electronic devices have played a major role. On the other hand, extensive use of such technology also has adverse effects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dep...

Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA).

BACKGROUND: Acute alcohol intoxication impairs cognitive and psychomotor abilities leading to various public health hazards such as road traffic accid...

Chestnut-Tannin-Crosslinked, Antibacterial, Antifreezing, Conductive Organohydrogel as a Strain Sensor for Motion Monitoring, Flexible Keyboards, and Velocity Monitoring.

Flexible sensing devices (FSDs) fabricated using conductive hydrogels have attracted researchers' extensive enthusiasm in recent years due to their ve...

Technique for Robotic Pancreaticojejunostomy.

BACKGROUND:: Minimally invasive, robotic techniques for hepatobiliary procedures offer the potential for accelerated recovery and reduced opioid usage...

Prediction of biphasic separation in CO absorption using a molecular surface information-based machine learning model.

Carbon dioxide capture technologies have become a focus to overcome global warming. Biphasic absorbents are one of the promising approaches for energy...

A strong and tough gelatin/polyvinyl alcohol double network hydrogel actuator with superior actuation strength and fast actuation speed.

Hydrogels are widely used in actuators that are applied in numerous fields such as multifunctional sensors, soft robots, artificial muscles, manipulat...

Using natural language processing to identify opioid use disorder in electronic health record data.

BACKGROUND: As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and co...

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. ...

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip.

Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, mo...

Predicting Low Cognitive Ability at Age 5-Feature Selection Using Machine Learning Methods and Birth Cohort Data.

In this study, we applied the random forest (RF) algorithm to birth-cohort data to train a model to predict low cognitive ability at 5 years of age a...

Early prediction of patient discharge disposition in acute neurological care using machine learning.

BACKGROUND: Acute neurological complications are some of the leading causes of death and disability in the U.S. The medical professionals that treat p...

Conformal prediction under feedback covariate shift for biomolecular design.

Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that ...

Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images.

BACKGROUND: Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the e...

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning.

Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved...

Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype.

OBJECTIVES: There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those obser...

A Smart Alcoholmeter Sensor Based on Deep Learning Visual Perception.

Process automation, in general, enables the enhancement of productivity, product quality, and consistency alongside other production metrics. Liquor p...

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush inj...

Predicting Substance Use Treatment Failure with Transfer Learning.

Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models ...

Addi-Reg: A Better Generalization-Optimization Tradeoff Regularization Method for Convolutional Neural Networks.

In convolutional neural networks (CNNs), generating noise for the intermediate feature is a hot research topic in improving generalization. The existi...