Pain Management

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study.

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the ...

Use of artificial neural networks to identify the predictive factors of extracorporeal shock wave therapy treating patients with chronic plantar fasciitis.

The purpose of our study is to identify the predictive factors for a minimum clinically successful t...

Hybrid Rehabilitation Therapies on Upper-Limb Function and Goal Attainment in Chronic Stroke.

This study examined the treatment effects between unilateral hybrid therapy (UHT; unilateral robot-a...

Applications of machine learning in addiction studies: A systematic review.

This study aims to provide a systematic review of the applications of machine learning methods in ad...

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.

IMPORTANCE: Current approaches to identifying individuals at high risk for opioid overdose target ma...

Simultaneous spatiotemporal tracking and oxygen sensing of transient implants in vivo using hot-spot MRI and machine learning.

A varying oxygen environment is known to affect cellular function in disease as well as activity of ...

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a...

Pilot Testing a Robot for Reducing Pain in Hospitalized Preterm Infants.

Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repe...

Addiction Theories and Constructs: a new series.

Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to ...

Hydroxytyrosol: The Phytochemical Responsible for Bioactivity of Traditionally used Olive Pits.

The fruits of L. is widely consumed as food, and olive pits are utilized in folk medicine to reliev...

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

BACKGROUND: Extracting relations between important clinical entities is critical but very challengin...

Natural Language Processing-Identified Problem Opioid Use and Its Associated Health Care Costs.

Use of prescription opioids and problems of abuse and addiction have increased over the past decade....

Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion.

BACKGROUND CONTEXT: The severity of the opioid epidemic has increased scrutiny of opioid prescribing...

Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication.

BACKGROUND AND OBJECTIVES: Clinical staff providing addiction treatment predict patient outcome poor...

The Importance of G-protein Coupled Estrogen Receptor in Patients With Fibromyalgia.

OBJECTIVES: This study aims to analyze the G-protein coupled estrogen receptor (GPER/GPR30) activity...

A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal.

Side effects occur when excessive or low doses of analgesics are administered compared to the requir...

Exercise-Associated Hyponatremia.

Exercise-associated hyponatremia (EAH) refers to below-normal serum sodium concentrations [Na+] that...

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in med...

Artificial neural networks in the selection of shoe lasts for people with mild diabetes.

This research addressed the selection of shoe lasts for footwear design to help relieve the pain ass...

Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prog...