Latest AI and machine learning research in sepsis for healthcare professionals.
OBJECTIVES: This study proposes a predictive model that integrates radiological features with multidimensional clinical factors for the accurate prediction of postoperative infection in maxillofacial fracture patients treated with absorbable fixation plates. METHODS: This model incorporates a fusion algorithm based on K-means clustering and fully connected neural networks to perform in-depth analy...
BACKGROUND: This study aimed to explore hub circadian rhythm-related genes (CRRGs) associated with sepsis-associated acute kidney injury (saAKI) using machine learning algorithms to provide novel ideas for the diagnosis and treatment of this disease. MATERIALS: Two AKI datasets (GSE30718 and GSE139061) were obtained from the Gene Expression Omnibus (GEO) database. We initially identified different...
PURPOSE: Studies of eye movements have evolved from clinical phenomenology to a probe of neural circuitry, cognition, and behavior offering a powerful...
Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...
Burn injuries are associated with significant morbidity and mortality, largely driven by infectious complications. Disruption of the skin barrier, sys...
RATIONALE: Sepsis is a state of life-threatening organ dysfunction in the setting of infection. It is biologically heterogeneous, as evidenced by whol...
OBJECTIVE: The rising incidence of Mycobacterium avium (MAV) infection poses significant challenges due to diagnostic delays and refractory treatment....
The neonatal intensive care unit (NICU) generates vast amounts of high-throughput, multimodal monitoring data, offering unprecedented potential for id...
The stable structure of biofilms and the characteristics of the bacteria within them make biofilms an important barrier for bacteria to resist externa...
Critically ill patients frequently require multiple concurrent interventions with complex interdependencies, yet existing prediction models treat thes...
In 2021, Maccabi Healthcare Services (MHS) introduced "UTI Smart-Set" (UTIS), an AI-driven decision support system (DSS) based on a machine-learning (...
PURPOSE OF REVIEW: Generative artificial intelligence, particularly large language models, has emerged as a promising tool for processing the vast amo...
Pathogenic antibiotic-resistant bacteria (PARB) pose a serious public health threat within the One Health framework, yet identifying their potential e...
BACKGROUND: Sepsis patients face a high mortality risk. Available prognostic biomarkers have certain limitations. This study explored the prognostic u...
BACKGROUND: Iron accumulation in the substantia nigra (SN) is a hallmark of Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) includ...
BACKGROUND: Large language models (LLMs) have potential to provide clinical infection advice, but variations in prevalent pathogens and antimicrobial ...
BACKGROUND: Early detection of sepsis in pediatric intensive care units (PICUs) is critical, but challenging due to its nonspecific clinical presentat...
BACKGROUND: Identifying communities disproportionately affected by hepatitis C infection is essential for targeted prevention and resource allocation....
BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to spec...
The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved saf...