Hospitalists

Artificial Intelligence in Total Hip and Knee Arthroplasty: A Primer on Current Applications, Algorithms, and Future Directions.

» Artificial intelligence (AI) is increasingly integrated across the total hip and knee arthroplasty care continuum, including preoperative risk stratification and templating, intraoperative computer-vision guidance and robotic assistance, and postoperative complication detection and outcome prediction. » Machine-learning models often outperform traditional statistical approaches in predicting com...

Early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion identified by artificial intelligence (ARCHES): study protocol for a multicentre randomised controlled trial.

BACKGROUNDS: Haematoma expansion (HE) is a significant factor in poor outcomes following intracerebral haemorrhage (ICH). Studies have suggested that acute intensive antihypertensive treatment could reduce HE. However, the impact of early intensive blood pressure reduction on patients with ICH at high risk of HE remains unclear. Therefore, screening ICH patients for high risk of HE upon admission ...

A multi-task learning framework for diagnosing partial discharge types and assessing severity.

Partial Discharge (PD) is one of the most critical factors contributing to the degradation of insulation systems in power transformers. Early detectio...

Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction.

BACKGROUND: Ventilator-induced diaphragm dysfunction (VIDD) is a frequent and under-recognized consequence of prolonged mechanical ventilation in inte...

Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial.

BACKGROUND: Large language models (LLMs) are increasingly explored as tools for medical education. However, evidence remains limited regarding their p...

HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data.

BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to spec...

A Multifunctional Eutectogel for Wide-Temperature and Intelligent Speech Recognition.

The practical application of flexible sensors is often constrained by limited mechanical properties and a narrow operating temperature range, particul...

Preoperative hypoalbuminemia predicts early complications, mortality, and discharge disposition following surgical resection of spinal metastases: a nationwide study.

PURPOSE: Patients undergoing surgery for spinal metastases often have limited physiologic reserve. Although hypoalbuminemia is a recognized risk marke...

An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.

The aim of this study is to develop and validate a machine learning-based predictive model to assess the risk of acquired bloodstream infection (BSI) ...

End to End Proximal Anastomosis is the Sole Modifiable Variable in Risk Score for Loss of Patency of Aortobifemoral Artery Bypass After Index Operative Hospitalization.

OBJECTIVE: The purpose of this study was to create a risk score for loss of aorto-bifemoral artery bypass (ABF) patency utilizing preoperative, periop...

AI Chatbot Answers for Drug Dosing Adjustments According to Renal Function in Geriatric Patients Using the New Scoring System (AI Quality Output Score): Cross-Sectional Study.

BACKGROUND: Preventable adverse drug reactions in geriatric patients are caused by overdosing, especially in cases of impaired renal function. Artific...

Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.

Sepsis is a high-burden, highly heterogeneous clinical challenge that affects up to 30% of ICU patients. Reliable early prediction is essential for ti...

Prediction of delirium in trauma patients using interpretable machine learning.

This study aimed to identify key risk factors for delirium in trauma patients and to develop an interpretable machine learning model using routinely a...

A Machine Learning Approach to Determine the Optimal Age for Total Hip Arthroplasty: When Is Risk for Adverse Outcomes Lowest?

INTRODUCTION: With evolving lifestyles and improvements in surgical techniques, the utilization of total hip arthroplasty (THA) is growing across pati...

Operational Integration and Temporal Validation of a Continuously Deployed ICU Prediction Model.

OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluatio...

Machine Learning-Assisted KCl-CaCl2-LiCl Electrolyte Design for Low-Temperature, High-Performance Calcium-Based Liquid Metal Batteries.

Calcium-based liquid metal batteries are promising for large-scale energy storage due to calcium abundance and low cost, yet their practical applicati...

Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.

BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and mortality, yet traditional screening tools such as the ...

Association of Deep Learning-Derived Temporalis Sarcopenia with Mortality in Acute Ischemic Stroke.

BACKGROUND: Sarcopenia is associated with mortality following acute ischemic stroke (AIS), but diagnosis is time-consuming. Computed tomography (CT) m...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Advancing Lithium-Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence-Driven Design Paradigms.

Lithium-oxygen batteries (LOBs) are regarded as one of the most promising next-generation energy storage systems, owing to their exceptionally high th...