Migraines

The Humanistic Basis of Psychoanalytic Practice.

This essay argues that psychoanalytic practice rests on a humanistic foundation irreducible to, and of a different order from, artificial intelligence. While large language models can simulate dynamic formulations, interpretations, and even therapeutic personas, such outputs are imitative; they retrieve and recombine rather than remember or desire. Drawing on three pivotal moments in history-Plato...

Capturing multi-disease states on a spectrum with machine learning and routine clinical data.

BACKGROUND: Diseases exist on spectra of risk factors, cellular perturbations, organ dysfunction, and clinical manifestations. It is unknown whether the analysis of routine laboratory tests and vitals using artificial intelligence presents a scalable and portable system for capturing the spectral nature of common diseases. METHODS: We constructed and validated machine learning models targeting sev...

Idiopathic Intracranial Hypertension Is Characterized by a Distinct Proteomic Profile.

OBJECTIVES: The pathophysiology of idiopathic intracranial hypertension (IIH) is poorly understood and disease-specific biomarkers are lacking. We aim...

'Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.'.

BACKGROUND: Headache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and c...

Machine Learning Model Predicts Clinical Adverse Events of Small Molecule Kinase Inhibitors in Cancer Patients Using On-/Off-Target Engagement and Tissue Selectivity.

Adverse events (AEs) of small molecule kinase inhibitors (SMKIs) at therapeutic doses in cancer patients are largely unpredictable in phase I-III stud...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...

Headache Diagnosis with Open Language Models on German Vignettes: Study Protocol.

Headache disorders present diagnostic challenges due to their clinical heterogeneity and the extensive taxonomy of the ICHD-3 classification. While la...

Guideline-Aligned Machine Learning for Predicting Ondansetron Administration at the End of Anaesthesia: Explainable Decision Support for PONV Prophylaxis.

Artificial Intelligence (AI) and Clinical Practice Guidelines (CPGs) both aim to support clinical decision-making but may provide conflicting suggesti...

Evaluating Medical Students' Perceptions of AI-Assisted Clinical Documentation (CarePilot): Cross-Sectional Study.

BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into health care to streamline documentation and improve clinician efficienc...

Adrenal gland dysfunction in males with cluster headache.

BACKGROUND: Cluster headache is associated with compensated hypogonadism in males, suggesting impaired testicular steroidogenesis. It is unknown if ad...

Impaired glymphatic function and advanced brain age gap in chronic migraine: a multimodal MRI study.

OBJECTIVES: This study investigated whether the coupling between cortical blood-oxygen-level-dependent (BOLD) signals and cerebrospinal fluid (CSF) fl...

Addressing the Treatment Gap for Pediatric Headache in Underserved Regions.

PURPOSE OF REVIEW: This review explores innovative strategies to address the treatment gap for pediatric headache disorders in underserved regions wor...

CT-based radiomic markers to predict late-onset seizures after traumatic brain injury.

Post-traumatic epilepsy (PTE) is a major long-term complication of traumatic brain injury (TBI), but early risk prediction remains imprecise. Radiomic...

NerveAI- a machine learning algorithm for detection of nerve pain in the head and neck.

Nerve pain screening in patients with headache disorders requires specialized clinical knowledge that is frequently not readily accessible at the init...

Divergent neuropsychiatric and systemic toxicity profiles of abemaciclib and palbociclib: a triangulation study integrating pharmacovigilance, genetic epidemiology, and multi-omics profiling.

Cyclin-dependent kinase 4/6 inhibitors improve outcomes in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breas...

Interpretable machine learning for postoperative nausea and vomiting prediction in elderly orthopedic patients: a comparative study.

BACKGROUND: Postoperative nausea and vomiting (PONV) prolongs hospitalization and reduces patient satisfaction. Identifying high-risk elderly patients...

Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.

BACKGROUND: Artificial intelligence (AI) is expanding across various medical fields, with machine learning (ML) being increasingly used to enhance pat...

Algometric assessment of sensitization and habituation as predictive markers for OnabotulinumtoxinA response in chronic migraine.

BACKGROUND: Chronic migraine is a debilitating disorder characterized by central sensitization and impaired habituation. Although OnabotulinumtoxinA (...

Machine learning-based prediction of Familial Hemiplegic Migraine risk from genetic variants.

BACKGROUND: Familial Hemiplegic migraine (FHM) is a rare migraine subtype characterised by transient unilateral motor weakness. Although familial form...

Pilot evaluation of a large language model-mediated pre-consultation interview for vestibular disorders.

OBJECTIVE: Efficient and high-quality history taking is central to vestibular diagnosis, but it is often constrained by limited consultation time and ...