Hospital-Based Medicine

AI-Driven Prediction of Multiple Outcomes in Older Adults with Coronary Heart Disease.

BACKGROUND AND OBJECTIVES: Frail older adults with coronary heart disease (CHD) face significantly elevated risks of adverse clinical outcomes, including mortality, prolonged hospitalizations, and frequent readmissions. Conventional risk stratification tools, inadequately account for frailty and multi-morbidity, limiting their effectiveness in geriatric care. To address this gap, we developed and ...

In situ trophic modes and metabolic traits of the dinoflagellate Karenia in response to short-term localized warming.

Algae primarily rely on photosynthesis, but many taxa can adopt mixotrophic strategies that integrate photoautotrophic and phago-heterotrophic nutrition under environmental stress. Despite growing recognition of mixotrophy, how short-term, localized warming analogous to marine heatwave-like thermal stress influences algal trophic strategies and metabolic traits remain poorly understood. Here, we c...

Predictive accuracy of Early Warning Score Systems for Detecting Critically Ill Patients in an Outpatient Setting.

Background No systematic methods exist for triaging outpatients with severe conditions. Our previous pilot study suggested that the National Early War...

Patterns of AI Use in Clinical Work by Hospitalists: Survey Study.

BACKGROUND: Artificial intelligence (AI) tools are widely and freely available for clinical use. Understanding hospitalists' real-world adoption patte...

In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight of Evidence Modeling.

BACKGROUND: Mortality prognostication in adult patients requiring extracorporeal membrane oxygenation (ECMO) is not accurate or established. We hypoth...

Fundamental understanding of electrochemical energy storage devices via in situ or operando characterization.

The importance of mechanistic studies in the field of energy storage cannot be understated. Electrochemical characterization alone cannot provide a co...

Using Machine Learning to Predict Features Within Substance Use Disorder Treatment Service Settings That Increase the Likelihood of Positive Treatment Outcomes.

Given the conceptual issues involved in defining and measuring recovery and accordingly substance use disorder (SUD) treatment outcomes, the role of e...

Collaborative Coarse-to-Fine Disease Learning With Discharge Summary Awareness for EHR Event Prediction.

Deep learning-based models have been widely used to predict electronic health record (EHR) events by exploiting diagnostic characteristics. Despite si...

A novel stacking ensemble model for predicting discharge coefficient of submerged multi parallel radial gates.

Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study.

BACKGROUND: Cardiorespiratory fitness (CRF) is a key predictor of cardiovascular and other health-related diseases in individuals with obesity. CRF is...

Advancing data science research education in Africa through datathon-driven innovations.

The rapid expansion of biomedical datasets generated by international research programs across Africa highlights an urgent need for advanced data scie...

Fairness analysis of machine learning predictions of aggression in acute psychiatric care.

Machine learning (ML) is increasingly being developed to support individualized risk assessment and de-escalation in acute psychiatry. However, ML alg...

Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.

Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits cons...

Artificial Intelligence Length-of-Stay Forecasting and Pediatric Surgical Capacity.

IMPORTANCE: Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high a...

Explainable machine learning prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage.

Accurate prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage (sICH) remains challenging. This study aimed to devel...

Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.

PURPOSE: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with ...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebr...

Updates in Central Retinal Artery Occlusion.

BACKGROUND: Central retinal artery occlusion (CRAO) is a vision-threatening neuro-ophthalmic emergency, analogous to acute ischemic stroke. Delayed pr...

Machine Learning-Driven Prognostic Model Integrating Lymphocyte-to-C-Reactive Protein Ratio and TNM Staging in Gallbladder Cancer.

BACKGROUND: A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing...