Hospitalists

Multiparametric AI-based perfusion analysis outperforms Tmax thresholding for critically hypoperfused tissue estimation in acute ischemic stroke undergoing admission MRI.

OBJECTIVE: Perfusion imaging is used to estimate critically hypoperfused tissue in acute ischemic stroke (AIS), commonly using threshold-based methods. This study compared an artificial intelligence (AI)-driven multiparametric approach with conventional thresholding for estimating ischemic core and hypoperfused tissue volumes. MATERIALS AND METHODS: We analyzed 186 AIS patients from the HIBISCUS-S...

How good are artificial intelligence tools at identifying benign skin lesions? A systematic review and meta-analysis of the specificity of artificial intelligence tools in diagnosing suspicious skin lesions.

BACKGROUND: Artificial intelligence (AI) is a transformative diagnostic tool in dermatology. As the prevalence of skin cancer rises and pressure on health services increases, there is an increasing demand for efficient diagnostic tools. Therefore, it is highly relevant to evaluate the diagnostic abilities of AI tools with a focus on not just the sensitivity, but also the specificity, to reduce unn...

Ensemble Machine Learning Models for Predicting Death in Post-Acute Skilled Nursing Facilities.

OBJECTIVE: Patients discharged from hospitals to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes, includin...

Comorbidity Burden and Length of Stay After Myocardial Infarction: A Retrospective Study Using Large Language Model-Assisted Chart Abstraction in a Tertiary Cardiac Hospital.

Patients' length of stay (LOS) during admission for myocardial infarction (MI) represents a closely tracked outcome metric for Cardiology services, wh...

Cross-sectional research: Application of an Artificial Intelligence-Based Pediatric Early Warning Score in the Pediatric Emergency Department.

BACKGROUND: There are a large number of pediatric emergency patients. Due to the fact that the children cannot describe their own conditions, there is...

Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?

BACKGROUND: In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoin...

Integrating ensemble and contrastive learning with explainable AI for modeling eutrophication-driven algal risks.

This study develops a Composite Eutrophication Index (CEI) based on four water-quality parameters to predict river eutrophication risk, using ten quan...

Machine learning-driven time series analysis for SOH prediction of lithium-ion batteries.

Energy storage batteries are essential for stabilizing renewable energy systems and improving power grid efficiency. However, challenges such as capac...

Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.

BACKGROUND: Accurate perioperative risk stratification is essential to patient safety and informed consent in spine surgery. Traditional regression-ba...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

Prediction of unplanned readmission in older adults during hospital stay: a data-driven approach to support care transition.

PURPOSE/AIMS: Accurately predicting 30-day unplanned readmission in older adults is critical for improving care transitions and reducing preventable h...

Consciousness Monitoring and Outcome Prediction Using EEG Connectivity in Severe Traumatic Brain Injury.

Neurological prognostication of patients in post-traumatic coma remains challenging due to the paucity of reliable markers in the acute phase. We aime...

An Introduction to Machine Learning for the Pediatric Hospitalist.

Machine learning models are increasingly used in clinical research to predict patient outcomes, yet many clinicians lack the training to critically ap...

Clinical Outcomes of Postedited Artificial Intelligence Translation for Discharge Instructions.

A dual-index framework for cold atmospheric plasma dosing: A meta-analysis of parametric control from volumetric redox flux to redox homeostatic status.

Cold atmospheric plasma (CAP) has emerged as a versatile therapeutic platform with demonstrated efficacy across diverse disease models. Despite signif...

Decision-making for ICU admission: is there a place for AI? Exploring and understanding meaning, experiences, and perspectives.

A multimodal predictive model incorporating transcriptomic-guided blood biomarkers and clinical variables for sepsis-associated acute kidney injury.

Sepsis-associated acute kidney injury (SA-AKI) is a major complication in the intensive care unit (ICU), and early risk stratification remains challen...

Deep learning‑based ultra-high-resolution CT imaging of viral pneumonia at admission and after discharge.

Predictive models for ICU patient readmission based on machine learning: A systematic review.

BACKGROUND: Artificial intelligence (AI) prediction models can accurately identify high-risk populations by integrating multi-dimensional clinical dat...

Physiology-informed machine learning for patient-level surgical decision-making in severe traumatic brain injury: Multicenter model development with external validation.

BACKGROUND: Surgical decisions for severe traumatic brain injury (TBI) are often made under prognostic uncertainty. Existing prognostic models predict...