Pain Management

Machine learning models to detect opioid misuse in emergency department patients at triage.

OBJECTIVE: Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few receive such care. Universal manual screening has been proposed to improve patient identification but is uncommon due to its time and resource-intensive nature. We sought to determine the feasibility of identifying patients with opioid misu...

[CefaleApp: A Mobile Application for Migraine Diagnosis in Primary Health Care].

INTRODUCTION: Headaches are the main reason for visits to Neurology clinics, and migraine is the most common primary headache. with migraine being the most frequent. Our objective was to develop a computer application (app) that could empower Primary Health Care (PHC) physicians in decision-making regarding migraine. MATERIAL AND METHODS: A rule-based artificial intelligence system was designed to...

Integrating Clinical Modeling and Machine Learning for Risk Assessment of Paracetamol and Other Nonsteroidal Anti-Inflammatory Drug Hypersensitivity in Children.

BACKGROUND: Nonsteroidal anti-inflammatory drug (NSAID) hypersensitivity is a common cause of drug-related reactions in children. Pre-test risk strati...

Predicting the Course of High-Impact Chronic Pain Using Machine Learning Algorithms.

High-impact chronic pain (HICP) affects over 17 million U.S. adults and follows highly variable courses. To date, the relative importance of biopsycho...

Deep learning the droughts: Spatiotemporal monitoring of protracted dry conditions in Queensland, Australia.

Protracted droughts, defined as successive dry events occurring before full vegetation recovery, pose an emerging challenge to assessing ecosystem res...

CPGPrompt: translating clinical guidelines into large language model-executable decision support.

OBJECTIVE: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial inte...

BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.

BACKGROUND: Accurate prediction of the neurotoxicity of peptides and proteins is critically important for the safety assessment of protein therapeutic...

Diagnostic Imaging Pathways in Peripheral Artery Disease: A Comparative Review of Duplex Ultrasound Through Computed Tomography Angiography.

Peripheral artery disease (PAD) is a major global health challenge, affecting more than 200 million people worldwide and an estimated 8 to 12 million ...

Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.

The treatment paradigm for lymphoma, a highly heterogeneous group of hematologic malignancies, has been revolutionized by the development of therapies...

The neurobiological cravings signature (NCS) as a predictive neuromarker of clinical outcomes in alcohol use disorder.

The Neural Craving Signature (NCS), a machine learning derived neuroimaging biomarker, differentiates individuals with from those without substance us...

Endometriosis pain index: development of a model to predict poor pain-related quality of life after endometriosis surgery through machine learning analysis of registry data.

Predictive tools are lacking for pain-related outcomes after endometriosis surgery. The objective of this study was to develop and validate a machine ...

Partial Data and Total Pain: Clinical and Ethical Hazards of AI Applications in Palliative Care.

Medicine finds itself on the brink of an artificial intelligence (AI) revolution, promising to transform what it means to be human and thus what it me...

Artificial Intelligence and Public Health Data in Emergencies: A Critical Issue in the Middle East.

Timely access to reliable public health data is a critical determinant of effective response to health emergencies, including disease outbreaks, clima...

Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.

The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficul...

Simulated evaluation of large language model stepwise diagnostic reasoning with real-world chest pain encounters and Bayesian networks.

BACKGROUND: Real-world evaluation of large language models (LLMs) as clinical diagnostic aids is limited by the reliance on static vignettes and retro...

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics.

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While te...

Hierarchical Channeled Graphitized Nanoarchitecture as a Diagnostic Platform for Maternal Fever Warning.

The precision and effectiveness of nano-diagnostic platforms rely on the deliberate design of advanced nanomaterials, aiming to address the clinical c...

Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning-Based Analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) poses significant challenges due to its association with increased cardiovascular...

Real-time intraoperative confocal laser endomicroscopy with on-site neuropathologist consultation for glioma margin assessment: illustrative case.

BACKGROUND: Achieving maximal safe resection in glioma surgery requires accurate real-time margin assessment, yet existing technologies have limitatio...

A transformer and 3D CNN-based feature fusion network with interpretable ability for Raman spectra analysis: improving the diagnosis of thyroid cancer.

Accurate differentiation of benign and malignant thyroid lesions continues to pose a significant clinical challenge. Raman spectroscopy offers label-f...