Latest AI and machine learning research in infection control for healthcare professionals.
Early hospital readmission in multimorbid patients remains a major clinical challenge. Although risk stratification tools are widely used, predictive performance is often limited. The PROFUND index captures frailty, functional dependence, and social vulnerability, but its role in predicting 30-day readmission is unclear. In this prospective multicentre cohort study, multimorbid patients admitted t...
Explainable Artificial Intelligence (XAI) has the potential to enhance clinical decision support (CDS) systems however, it remains unclear how XAI systems are perceived by healthcare professionals in hospital settings, and if new challenges arise as a result of explanations. This scoping review aimed to understand healthcare professionals' perceptions of CDS systems with XAI in the hospital settin...
PURPOSE: To develop and validate machine learning (ML) models for predicting early postoperative corneal edema (CE) after phacoemulsification in patie...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk strati...
BACKGROUND: Acute kidney injury (AKI) is a common and serious complication among hospitalized patients, and early risk stratification remains challeng...
The use of certain artificial intelligence (AI) tools may improve hospital operational efficiency, in particular in overcrowded emergency departments ...
BACKGROUND: Viral encephalitis (VE) is an acute inflammatory disease caused by viral infection. Children are at a significantly higher risk of develop...
BACKGROUND: Critically ill patients with ischemic stroke face substantial in-hospital mortality. Early and accurate prediction of mortality risk may f...
Acute kidney injury (AKI) is a common hospital complication with substantial morbidity and mortality. Deep learning models for AKI prediction show str...
BACKGROUND: Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machin...
The rise of artificial intelligence (AI) has introduced new possibilities for hospital and clinic libraries. A research project surveyed hospital and ...
BACKGROUND: The expansion of digitalization in the pre-, intra- and post-operative surgical phases allow the development and integration of advanced t...
BACKGROUND: Automatic segmentation of gliomas on amino acid PET is essential for quantitative tumor assessment, a pillar in monitoring gliomas under t...
This study aimed to develop an interpretable machine learning model for predicting in-hospital mortality among acute ischemic stroke (AIS) patients ad...
BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent adva...
PURPOSE: To evaluate the effectiveness and usability of a safety-first, clinician-validated conversational artificial intelligence (AI) chatbot for ca...
BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surv...
AIM: Planning for a hospital is a complex and dynamic process, traditionally not informed by evidence. A systems thinking approach can be useful in in...
RATIONALE & OBJECTIVE: Acute kidney injury (AKI) in research is typically identified using KDIGO criteria based on changes in serum creatinine (SCr) l...
INTRODUCTION: Typhoid intestinal perforation (TIP) remains a significant cause of pediatric morbidity in resource-limited settings, with prolonged hos...