Product Alert

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is an emerging biomarker of biological ageing linked to mortality. This study assessed its prognostic value for short- and long-term mortality after coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), addressing model bi...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to false-positive findings and unnecessary biopsies. Radiomics offers a quantitative approach to improve lesion characterization by capturing tumor-related image-based features. In this study, we explored the application of treatment response assessment map...

Machine Learning-Enhanced TCAB Score for Predicting Postoperative Ischemic Stroke After CABG.

BACKGROUND: Postoperative acute ischemic stroke remains a critical complication of coronary artery bypass grafting. This study aimed to develop a nove...

Integrating ChatGPT into knowledge-retrieval tutorials in undergraduate medical education: a prospective evaluation of higher-order learning and feasibility.

BACKGROUND: Generative artificial intelligence (AI) platforms such as ChatGPT present new opportunities to strengthen competency-based medical educati...

DeepArousal-Net: A Multi-Block Recurrent Deep Learning Model for Proactive Forecasting of Non-Apneic Arousals From Multichannel PSG.

OBJECTIVE: This study aimed to develop a deep learning model capable of accurately forecasting non-apneic sleep arousals, which are brief awakenings t...

Adverse reactions of PD-1/PD-L1 inhibitors in cancer: FAERS database analysis and protocols to mitigate immune-related events in elderly patients and when using pembrolizumab and atezolizumab.

OBJECTIVE: This study aimed to characterize adverse drug reactions (ADRs) associated with programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) in...

Exploring factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients based on pre-dialysis parameters.

OBJECTIVE: This study explored factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients (MHD) based on ...

Maternal and Pediatric Precision in Therapeutic Knowledge Portal (MPRINT-KP): Landscape Analysis of Pharmacology Research in Maternal and Pediatric Patient Populations.

Pregnant women and children have been underrepresented in clinical studies due to ethical concerns and perceived vulnerabilities. This resulted in a s...

Predicting seizure-free outcomes in people with treatment-resistant epilepsy: A machine learning approach.

BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...

Artificial intelligence revolution in toxicology: Clinical precision, global equity, and the 2030 roadmap.

Artificial intelligence (AI) is transforming toxicology by enabling faster, more accurate, and more equitable approaches to diagnosis, treatment, rese...

NLP in Support of Pharmacovigilance: QUality Adverse Drug Reaction AcTIve Control (QUADRATIC).

Adverse drug reactions (ADRs) are a major cause of morbidity, hospital admissions, and in-hospital mortality, yet remain incompletely captured by post...

Embedding Artificial Intelligence Competencies in Military Medical Education: A Longitudinal Framework.

Artificial intelligence (AI) is rapidly transforming healthcare delivery, logistics, and operational decision-making across the Military Health System...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

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...

New treatments to rabies virus infections.

Rabies disease is the deadliest of all virus infections. Till now, vaccination is the most effective and reliable treatment for controlling rabies. Ho...

Exploring key risk factors for loss to follow-up after hospitalization for acute stroke.

BACKGROUND: A significant proportion of stroke patients are lost to follow-up (LTFU) after discharge, which may increase risks of morbidity, mortality...

Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.

BACKGROUND: Deep learning has shown promise in diabetes management but faces challenges in real-world application due to its "black-box" nature, chara...

Effectiveness of transcranial direct current stimulation (tDCS) on attention and memory biases in individuals with post-traumatic stress disorder (PTSD): a randomized, single-blind, parallel-group study.

Based on neurocognitive models, the development and maintenance of post-traumatic stress disorder (PTSD) are correlated with cognitive biases, includi...

Integrated virtual screening, machine learning and molecular dynamics identify novel phytochemical FabI inhibitors against MRSA.

Methicillin-resistant Staphylococcus aureus (MRSA) infections pose a critical global health threat, necessitating the discovery of novel antibacterial...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...