Hospital-Based Medicine

Explainable Prediction of ICU Transfer in Acute Pancreatitis: A Neural Network Model With SHAP Interpretability for Clinical Decision Support.

OBJECTIVE: Acute pancreatitis (AP) is a life-threatening disorder commonly observed in emergency departments. Patients with acute pancreatitis may necessitate transfer to the intensive care unit (ICU) if standard treatments prove ineffective. The development of a machine learning (ML) model that can precisely forecast the necessity for ICU admission in patients with acute pancreatitis might dramat...

Risk factors and early clinical outcomes of acute pancreatitis: A retrospective case series from Kyrgyzstan.

BACKGROUND: Acute pancreatitis (AP) is a common gastrointestinal emergency characterized by unpredictable severity. Early identification of patients at risk for severe disease is essential for timely intervention and improved outcomes, yet reliable prognostic markers remain limited, particularly in Central Asian clinical settings. OBJECTIVE: To identify early clinical, laboratory, and demographic ...

Ardent Health: An AI-Enabled Virtual Care Model, from Pilot to Production.

Healthcare leaders face sustained uncertainty: workforce volatility, financial pressure, and accelerating technology change. In 2024-2025, Ardent Heal...

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI): Prediction of Inpatient Admission From Emergency Departments Using Ensemble Machine Learning.

OBJECTIVE: Use artificial intelligence (AI) to extend the Sydney triage to admission risk tool (START) and improve prediction of emergency department ...

Assessment of the risk of bacteremia in patients with hematologic malignancies in the emergency department: A comparative study between logistic regression and machine learning algorithms.

OBJECTIVE: To stratify the risk of bacteremia at the time of emergency department (ED) admission in patients with hematologic malignancies. To this en...

Real-time prediction of emergency department admissions using an AI model and its integration into hospital bed-planning.

OBJECTIVE: To design, validate, and implement a tool based on a machine-learning model capable of predicting emergency patient admissions in real time...

Impact of a Digital Intelligence Platform on Radiology Workflow Efficiency and Patient Satisfaction: A Retrospective Cohort Study.

RATIONALE AND OBJECTIVES: Traditional radiology management models may face challenges in meeting the growing demand for medical imaging services, pote...

Arrhythmias and cardiac devices management in oncology patients.

Cardiac arrhythmia is increasingly encountered in patients with cancer, not only as a result of shared risk factors but also as a direct consequence o...

Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.

Early identification and prevention of persistent acute kidney injury (pAKI) remain challenging due to delayed biochemical markers and limited tools t...

Electronic structure manipulation in electrocatalysis: mechanisms and design principles.

Electrocatalysis is a novel technology that can convert intermittent renewable electrical energy into high-value chemical fuels or products, achieving...

Artificial intelligence-assisted detection of epileptic spasms using electroencephalographic-video analysis.

OBJECTIVE: This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG...

Artificial Intelligence-Assisted Ultrasound in Austere Trauma Environments: Implications for Role 1 Military Medicine.

Pararescue jumpers are United States Air Force medical tactical operators who provide advanced trauma and prolonged casualty care in austere, high-ris...

Optimizing Machine Learning-Based Diagnosis of Neurosyphilis in HIV-Negative Patients: A Multicenter, Real-World Comparison of International Diagnostic Criteria.

AbstractNeurosyphilis continues to rise globally, yet diagnosis remains challenging, often requiring multidisciplinary expertise and multiple CSF assa...

Incorporating Patient Similarity and Clinical Temporality in Disease Prognostic Modeling.

Health recommender systems (HRSs) enhance prognostication by leveraging clinical information. Existing HRSs often fail to capture the intrinsic correl...

Improving ward round documentation using the Heidi Health application.

INTRODUCTION: Accurate and timely documentation during surgical ward rounds is critical for ensuring patient safety, effective multidisciplinary commu...

Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

BACKGROUND: Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with...

A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools.

BACKGROUND: Clinical Decision Support (CDS) tools integrated with Electronic Health Records increasingly guide clinical practice. Epic Systems, storin...

Human vs. artificial intelligence in medical charting: a comparative study in the simulated emergency medicine context.

OBJECTIVES: The increasing administrative burden associated with electronic health records has contributed to reduced efficiency and rising burnout am...

[Pulmonary embolism response team (PERT): development, implementation, and clinical impact].

Pulmonary embolism (PE) is a common and potentially fatal venous thromboembolic disease. Traditional management paradigms, often characterized by insu...

Machine learning to predict hospital admission at triage in paediatric emergency care: A meta-analysis.

UNLABELLED: Machine learning (ML) models have shown promise improving outcome prediction and early risk stratification in paediatric emergency departm...