Background Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous
patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has …

Integrating machine learning (ML) into intensive care units (ICUs) can significantly enhance
patient care and operational efficiency. ML algorithms can analyze vast amounts of data …

Background: Artificial intelligence (AI) has garnered considerable attention in the context of
sepsis research, particularly in personalized diagnosis and treatment. Conducting a …

Objective To develop and externally validate models based on neural networks and natural
language processing (NLP) to identify suspected serious infections in emergency …

Clinical decompensation and unexpected adverse events occur in roughly 10% of hospital
admissions 1–3 despite decades of preventive efforts and patient safety initiatives. 4–6 A life …

The biomedical field is among the sectors most impacted by the increasing regulation of
Artificial Intelligence (AI) and data protection legislation, given the sensitivity of patient …

Objective Acute pancreatitis (AP) progresses to septic shock can be fatal. Early identification
of high-risk patients and timely intervention can prevent and interrupt septic shock. By …

Real-world performance of machine learning (ML) models is crucial for safely and effectively
embedding them into clinical decision support (CDS) systems. We examined evidence …

Objective To investigate the impact of missing laboratory measurements on sepsis
diagnostic delays. Materials and Methods In adult patients admitted to 2 University of …

Introduction Identifying patients with at a high risk of progressing to septic shock is essential.
Due to systemic vasodilation in the pathophysiology of septic shock, the use of diastolic …