Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
INTRODUCTION: Describe a methodological proposal and value-based ratio calculations based in real-world hospital-care episodes in Spain, serving as cost per outcomes benchmarking references. METHODOLOGY: Patient Reported Experience/Outcome Measures (PREMs/PROMs) as well as professional opinions were collected. A first data collection took place (January/June-2023), six months after knee (KP) and h...
BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare, including pediatrics, offering new opportunities for diagnosis, management, and decision support. However, the effective implementation of AI depends largely on healthcare professionals' knowledge, attitudes, and readiness to adopt these technologies. This study aimed to evaluate pediatricians' general attitudes t...
BACKGROUND: Heart failure is not only a prevalent disease with a high mortality rate, but also generates high costs for healthcare systems. By trainin...
BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is currently a lack of precise early prediction models. T...
INTRODUCTION: With evolving lifestyles and improvements in surgical techniques, the utilization of total hip arthroplasty (THA) is growing across pati...
OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluatio...
Calcium-based liquid metal batteries are promising for large-scale energy storage due to calcium abundance and low cost, yet their practical applicati...
BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the ...
BACKGROUND: Emergency department (ED) visits have risen in the United States, with demand for emergency care exceeding supply. Resultant ED crowding h...
BACKGROUND: Artificial intelligence (AI) is rapidly transforming health care and health research, offering new opportunities for improving efficiency,...
OBJECTIVE: To develop and internally validate an explainable machine learning model for predicting textbook outcome (TO) after free flap reconstructio...
BACKGROUND: Sarcopenia is associated with mortality following acute ischemic stroke (AIS), but diagnosis is time-consuming. Computed tomography (CT) m...
PURPOSE: This narrative review summarizes an artificial intelligence (AI)-integrated digital workflow that enables a seamless, multidisciplinary appro...
BACKGROUND: Conventional clinical scoring systems and contrast-enhanced computed tomography (CECT) interpretation provide limited accuracy in predicti...
BACKGROUND: Severe trauma remains a leading cause of admission to the intensive care unit. The Trauma and Injury Severity Score (TRISS) is an establis...
BACKGROUND: Maternal anaemia remains a pressing global health challenge, with a notable burden in low- and middle-income countries. Existing studies i...
Machine learning (ML) offers opportunities to improve prognostication after ST-segment elevation myocardial infarction (STEMI), but real-world registr...
PURPOSE: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical...
PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...
OBJECTIVES: This study aims to provide a comprehensive bibliometric mapping of the scientific evolution and research trends of fractal analysis (FA) i...