Pain Management

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medical infrastructure. In this study, we propose a comprehensive approach to extract mortality causes and identify prevalent diseases from VA narratives utilizing advanced text mining techniques, so as to better understand the underlying health issues l...

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorith...

MyofAPPcial: Construct validity of a novel technological aid for improving clinical reasoning in the management of myofascial pain syndrome.

BACKGROUND: Physiotherapists encounter challenges in diagnosing myofascial trigger points (MTrPs), which are crucial for managing myofascial pain but ...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning...

Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The ...

Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.

Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature...

GlobalSR: Global context network for single image super-resolution via deformable convolution attention and fast Fourier convolution.

Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the...

AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol.

INTRODUCTION: This paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologi...

Cross-view discrepancy-dependency network for volumetric medical image segmentation.

The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the...

Image harmonization with Simple Hybrid CNN-Transformer Network.

Image harmonization seeks to transfer the illumination distribution of the background to that of the foreground within a composite image. Existing met...

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable ...

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.

CLINICAL RELEVANCE: Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challeng...

A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks ...

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treati...

ASF-LKUNet: Adjacent-scale fusion U-Net with large kernel for multi-organ segmentation.

In the multi-organ segmentation task of medical images, there are some challenging issues such as the complex background, blurred boundaries between o...

Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physi...

Health inequities, bias, and artificial intelligence.

Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and disability globally. Due to its complex etiology, ...

Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features.

OBJECTIVE: To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preve...

The use of artificial neural networks in studying the progression of glaucoma.

In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilit...