Hospitalists

Point-of-Care Artificial Intelligence Measure of Seizure Burden Associates With Clinical Outcome at Discharge.

OBJECTIVES: Point-of-care (POC) electroencephalography (EEG) enabled with artificial intelligence (AI) algorithms hold the potential to address gaps in EEG access and interpretation. We assessed the clinical association of the patterns recognized by one of such systems. DESIGN: Secondary cohort analysis of the retrospective multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (S...

Association between the development of sepsis and the BAR index in patients with acute pancreatitis: A retrospective investigation based on MIMIC-IV database.

Sepsis, as a severe complication of acute pancreatitis (AP), needs to be identified and treated as early as possible. The blood urea nitrogen-to-albumin ratio (BAR) index has shown good predictive ability in many studies. However, its effectiveness in predicting AP complicated by sepsis has not been further verified. Therefore, this study mainly discusses the correlation between BAR and the risk o...

Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study.

BACKGROUND: The aim of this study was to evaluate the effectiveness of two general-purpose Large Language Models (LLMs), ChatGPT and Gemini, in predic...

Language barriers, mobility, and health equity: clinical risks, economic burden, and policy options for universal language access.

BACKGROUND: International mobility has intensified routine contact between health systems and patients who do not speak the dominant language of care....

Machine Learning-Based Periprocedural Prediction Model for No-Reflow Risk After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome.

Coronary no-reflow (NR) after percutaneous coronary intervention (PCI) predicts adverse prognosis in patients with acute coronary syndrome (ACS). This...

AI-Generated Avatar Videos for Postoperative Patient Education Among Health Care Workers: Pilot Randomized Controlled Trial.

BACKGROUND: Effective postoperative communication is vital for patient recovery, yet traditional text-based discharge instructions often lead to poor ...

Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.

OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progress...

Evaluating large language models for structuring cardiology reports: a real-world clinical study on patient subtyping and trial recruitment.

BACKGROUND: Artificial Intelligence (AI) methods have emerged as useful tools for supporting patient recruitment in clinical trials (CTs). Despite sev...

Electrical discharge machining of Ti6Al4V in deionized water: surface integrity and wear response for medical applications.

Titanium alloys are widely used in medical applications, particularly in dentistry and orthopedics, due to their biocompatibility, corrosion resistanc...

Diagnostic utility of the platelet-to-thrombin-antithrombin complex ratio in heatstroke: a single-center retrospective analysis.

PURPOSE: To assess the discriminatory performance of the platelet-to-thrombin-antithrombin complex ratio (PLT/TAT) for differentiating heatstroke from...

Optimizing heart failure care: A machine learning-based prediction of hospital length of stay for heart failure patients.

BACKGROUND: Heart failure represents a significant global health burden, with prolonged length of stay (LoS) tied to increased mortality and costs. Ac...

Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.

Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aime...

Beyond readability: Advancing patient-centered evaluation of AI-generated clinical reports.

Large language models offer promising opportunities to simplify clinical documentation and improve the accessibility of medical information for patien...

The SUPREME Index for 30-day all-cause readmission: development and internal validation of a machine learning-based risk stratification index in community-dwelling adults.

BACKGROUND: Hospital readmissions are a key indicator of healthcare quality and have substantial implications for patient outcomes and system-level co...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Prediction of 30-Day All-Cause Hospital Readmissions Using Limited Structured Electronic Health Record Data: Retrospective Comparative Study.

BACKGROUND: Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, wit...

Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.

BACKGROUND: Infections are a leading cause of hospitalizations and emergency department (ED) visits in home care. Existing prediction tools often unde...

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Despite the advances in critical care and innovations of medical technology, earlier identification of children at high mortality risk remains challen...

A Two-Stage Pipeline for Linking Clinical Notes to SNOMED CT.

Extracting clinically useful information from free-text notes remains challenging due to their unstructured nature, while medical coding is still only...

Evidence-Grounded LLM Validation of MIMIC-IV ICD Labels.

Automatically assigning ICD-10 diagnosis codes from discharge summaries is a central multi-label task in clinical NLP, yet widely used benchmarks such...