Addictions

Multi-label classification and label dependence in in silico toxicity prediction.

Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance of 3 multi-label classification (MLC) models, namely Classifier Chains (CC), Label Powersets (LP) and Stacking (SBR), against independent classifiers ...

Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patients.

Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio.

The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diaze...

Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects.

Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimen...

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algo...

Silver nanoparticle on zinc oxide array for label-free detection of opioids through surface-enhanced raman spectroscopy.

Opioid abuse is a significant public health problem. Over two million Americans have some form of addiction to opioids; however, despite governmental ...

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is...

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning.

The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk ...

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in ...

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening me...

Repeatability and reproducibility study of radiomic features on a phantom and human cohort.

The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imagi...

Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery.

After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process ...

An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity.

PURPOSE: In people with obesity, food addiction (FA) tends to be associated with poorer outcomes. Its diagnosis can be challenging in primary care. Ba...

Lower, Variable Intrathecal Opioid Doses, and the Incidence of Prolonged Fetal Heart Rate Decelerations After Combined Spinal Epidural Analgesia for Labor: A Quality Improvement Analysis.

BACKGROUND: Combined spinal-epidurals with low-dose intrathecal opioids and local anesthetics are commonly used to initiate labor analgesia due to the...

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.

Drug-induced liver injury (DILI) is the most frequently reported single cause of safety-related withdrawal of marketed drugs. It is essential to ident...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women'...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system.

Application of machine-learning methods to assess the batch adsorption of malachite green (MG) dye on chitosan/polyvinyl alcohol/zeolite imidazolate f...

Trace element, immune and opioid biomarkers of unstable angina, increased atherogenicity and insulin resistance: Results of machine learning.

BACKGROUND: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The ro...

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain...