Pain Management

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 therapy after extracorporeal shock wave therapy for chronic plantar fasciitis. The demographic and clinical characteristics were evaluated. The artificial neural networks model was used to choose the significant variables and model the effect of achieving the minimum clinically successful therapy at ...

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-assisted therapy [RT] + modified constraint-induced movement therapy) and bilateral hybrid therapy (BHT; bilateral RT + bilateral arm training) compared with RT. Thirty patients with chronic stroke were randomized to UHT, BHT, or RT groups. Preliminary efficacy was assessed using the Fugl-Meyer Asses...

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 addiction research. In this study, multiple searches...

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 many patients who are not truly at high risk.

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 spectrum of tauopathies. These tauopathies have d...

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

Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repeated procedural pain is associated with adverse ef...

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 exert their analgesic effects. The most commonly p...

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 relieve gastric disturbances. In the present study, the ...

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. Claims-based studies have documented substantial ...

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 practices. Spine surgery is a high-risk episode f...

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 poorly. Prognoses based on linear statistics are rarel...

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 in patients with fibromyalgia syndrome (FMS).

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 required amount to mediate the pain induced during surge...

Exercise-Associated Hyponatremia.

Exercise-associated hyponatremia (EAH) refers to below-normal serum sodium concentrations [Na+] that develop during exercise. The pathogenesis of EAH ...

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 medical data classification studies. LR, a statistica...

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 associated with diabetic neuropathy and foot ulcers. ...

Prediction of TF-Binding Site by Inclusion of Higher Order Position Dependencies.

Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to e...

EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based o...

Support vector machine-based multivariate pattern classification of methamphetamine dependence using arterial spin labeling.

Arterial spin labeling (ASL) magnetic resonance imaging has been widely applied to identify cerebral blood flow (CBF) abnormalities in a number of bra...