Pain Management

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.

Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors.

BACKGROUND: Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFUs), which severely affect patients' quality of life. This study aimed to develop machine learning (ML) predictive models for DPN and LEAD and to identify both shared and distinct risk factors.

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly ...

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mor...

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversib...

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches.

Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but information about their transformation products (...

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed mach...

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and e...

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

BACKGROUND: Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific me...

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial.

BACKGROUND: Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we teste...

Application of Artificial Intelligence in the Headache Field.

PURPOSE OF REVIEW: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded hea...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder...

SymTC: A symbiotic Transformer-CNN net for instance segmentation of lumbar spine MRI.

Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this d...

A CNN-CBAM-BIGRU model for protein function prediction.

Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this chal...

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder.

BACKGROUND: Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcomes; however, retention is essential.

Detection and Localization of Spine Disorders from Plain Radiography.

Spine disorders can cause severe functional limitations, including back pain, decreased pulmonary function, and increased mortality risk. Plain radiog...

Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning.

BACKGROUND: Acetaminophen (APAP) is commonly used as an antipyretic analgesic. However, acetaminophen overdose may contribute to liver injury and even...

Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning.

Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis...

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for...

Self-help groups and opioid use disorder treatment: An investigation using a machine learning-assisted robust causal inference framework.

OBJECTIVES: This study investigates the impact of participation in self-help groups on treatment completion among individuals undergoing medication fo...