Latest AI and machine learning research in infection control for healthcare professionals.
Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with acute myocardial infarction (AMI) contributing to over 100,000 deaths annually in the United States. Accurate risk stratification for in-hospital mortality is essential for guiding clinical decisions, improving outcomes, and optimizing hospital resources. However, existing models often rely on limited predictor set...
The privacy protection of medical patients has remained a critical concern in healthcare information management during the digital era. Conventional approaches have predominantly relied on rule-based protocols and data encryption systems, which typically require substantial involvement of IT professionals for implementation. Recent advancements in Large Language Models (LLMs) have introduced novel...
Patients often struggle to fully understand their discharge letters after inpatient hospital stays, which are often replete with domain-specific medic...
Adequate self-harm surveillance is a key part of suicide prevention efforts. Our prior work has demonstrated the efficacy of an artificial intelligenc...
The timely detection of ward deterioration—including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis—remains an unmet ...
Normal pressure hydrocephalus (NPH) is a potentially treatable neurodegenerative disorder that remains underdiagnosed due to its clinical overlap with...
Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...
Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...
While machine learning (ML) models show strong performance for predicting unplanned hospital visits, their clinical utility relative to physician judg...
Antimicrobial resistance (AMR) poses a significant public health challenge, particularly in resource-limited settings such as Zimbabwe, where surveill...
Prognostication in patient with out-of-hospital cardiac arrest (OHCA) underwent extracorporeal cardiopulmonary resuscitation (ECPR) remains challengin...
Control of blood pressure (BP) continues to be a challenge globally. Clinical trials have shown home BP monitoring and text-message interventions to l...
To evaluate the reliability and generalization of NeoNaid, a fully automated software tool for neonatal EEG analysis, based on functional brain age (F...
Artificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, bu...
Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Uns...
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperati...
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The ad...
BACKGROUND: The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a t...
This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Levera...
Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-disc...