Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Hospital artificial intelligence (AI) and robotics are spreading unevenly across the United States, yet national evidence on how these technologies are associated with hospital performance and population health remains limited. This observational study linked the 2024 American Hospital Association Annual Survey, capturing calendar-year 2023 adoption across 6,166 hospitals, to CMS, CDC, and County ...
Abstract Background: Echocardiography (echo) notes contain valuable prognostic information for patients in the intensive care unit (ICU). However, their unstructured format and the presence of sensitive patient information present challenges for large-scale, automated analysis. There is a need for secure and efficient methods to extract and utilize echo data to enhance ICU outcome prediction. Meth...
Background: Previous research has shown that radiomics-based machine learning models are promising precision medicine tools for lesion-level predictio...
Background: Cardiovascular disease (CVD) readmissions impose substantial clinical and economic burden. Machine learning (ML) may improve risk stratifi...
Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet hete...
Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare...
Rationale Autonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification remains challenging. Multiscale entropy (MSE) derived ...
Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language model...
Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS England reporting over 48,000 sepsis-related deaths ...
Pediatric asthma exacerbations are a frequent cause of emergency department (ED) visits and hospitalizations, yet accurate risk prediction remains lim...
Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. Howe...
Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely au...
Overcrowding of emergency departments (ED) is now a problem of global health care concern due to the increase in patients. Triage systems have been es...
Rapid risk stratification is essential in the clinic, yet vital signs, laboratory tests, and triage scores may not fully capture risk at presentation....
Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...
Objective: We developed and validated a detection-guided artifact removal framework for clinical electroencephalography (EEG). The framework applies a...
Background: Nursing documentation patterns may reflect patient acuity and clinical deterioration, yet their prognostic value remains underexplored. We...
Importance: High-quality discharge summaries are essential for safe care transitions but contribute substantially to clinician documentation burden an...
Abstract Objective More people than ever before are living with cancer. Patient education is a core component of cancer care, and patients are increas...
Background: Prognostic assessment in critically ill patients with cancer remains challenging, as conventional ICU severity scores often perform subopt...