AIMC Topic: Middle Aged

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Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method.

BMC emergency medicine
BACKGROUND: Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate prediction of difficult laryngoscopy is essential for improving first-attempt success, minimizing complicatio...

New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.

Scientific reports
Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyze...

Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

Scientific reports
While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy (ECT) remains rather limited. With the advent of digital seizure collection systems, the developmen...

Prioritizing Trust in Podiatrists' Preference for AI in Supportive Roles Over Diagnostic Roles in Health Care: Qualitative Interview and Focus Group Study.

JMIR human factors
BACKGROUND: As artificial intelligence (AI) evolves, its roles have expanded from helping out with routine tasks to making complex decisions, once the exclusive domain of human experts. This shift is pronounced in health care, where AI aids in tasks ...

Perspectives of Black, Latinx, Indigenous, and Asian Communities on Health Data Use and AI: Cross-Sectional Survey Study.

Journal of medical Internet research
Despite excitement around artificial intelligence (AI)-based tools in health care, there is work to be done before they can be equitably deployed. The absence of diverse patient voices in discussions on AI is a pressing matter, and current studies ha...

Artificial intelligence in neurovascular decision-making: a comparative analysis of ChatGPT-4 and multidisciplinary expert recommendations for unruptured intracranial aneurysms.

Neurosurgical review
In the multidisciplinary treatment of cerebrovascular diseases, specialists from different disciplines strive to develop patient-specific treatment recommendations. ChatGPT is a natural language processing chatbot with increasing applicability in med...

Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.

Frontiers in public health
BACKGROUND: It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed f...

Actigraphy against 32-hour polysomnography in patients with suspected idiopathic hypersomnia.

Journal of sleep research
Actigraphy, a tool known for investigating sleep-wake patterns at home, lacks scientific validation in hypersomnolent subjects. We aim to validate an actigraphy-based sleep-wake prediction algorithm against 32-h continuous polysomnography in patients...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

Clinical lymphoma, myeloma & leukemia
BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however, durable complete responses are achieved in only 30% to 40% of patients. Additionally, CAR-T therapy...