Hospitalists

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admission-time risk stratification tools are lacking. We aimed to characterize HACs and develop both an ensemble prediction model and a simplified bedside risk score. METHODS: This retrospective cohort study used the Japanese Diagnosis Procedure Combinatio...

A pilot randomized controlled trial protocol of overground robotic exoskeleton-assisted early ambulation after cardiovascular and thoracic surgery in the acute care setting (REACT-CVTS).

Early ambulation following cardiovascular and thoracic surgery (CVTS) is associated with improved postoperative outcomes. However, barriers such as patient debility, high acuity, hemodynamic instability, cardiopulmonary complications, or resource limitations (e.g., intensive care unit setting, specialized equipment, or staff training) often prevent or delay early ambulation. Overground robotic exo...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...

Clinician-supervised large language AI model after-discharge instruction generation for common emergency department presentations.

OBJECTIVES: After-discharge instructions often fail due to poor usability and language misalignment. We evaluated a clinician-supervised method for ge...

An explainable artificial intelligence framework for clinical decision support in stroke discharge planning.

BACKGROUND: Stroke, a leading cause of global mortality and disability, requires accurate prediction of discharge outcomes to support early care plann...

Predicting Patient-Reported Outcome Measures, Satisfaction, Healthcare Utilization, Mortality, and Return to Work After Total Knee Arthroplasty Using Machine Learning: A 14,900-Patient Study.

BACKGROUND: Patient-reported outcome measures and healthcare utilization metrics are increasingly used to evaluate the success of total knee arthropla...

Seed-to-Semantics: Few-shot Prototype-Guided Progressive Learning for Hyperspectral and LiDAR Classification.

Deep learning-based fusion of hyperspectral images (HSI) and LiDAR has achieved strong performance in multimodal remote sensing classification, but it...

A PCA-enhanced XGBoost framework for combined SOC and SOH estimation in lithium-ion batteries.

Accurate estimation of State of Charge (SOC) and State of Health (SOH) is critical for safe and reliable operation of lithium-ion batteries under temp...

Machine learning driven modeling of synergistic perinatal risk profiles in early onset pediatric cerebral palsy.

AIM: To develop and evaluate machine learning (ML) models for early cerebral palsy (CP) prediction and identify synergistic perinatal risk factors in ...

Development of a machine learning-based model for predicting adverse pregnancy outcomes in women with polycystic ovary syndrome (PCOS): a retrospective observational study protocol.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a prevalent endocrine condition in reproductive-aged women, which is associated with adverse materna...

Nursing Leadership for Digital NICU Transformation: A Systematic Review of Strategies, Digital Health Integration, and Outcomes in High-Risk Neonates.

BACKGROUND: Digital health technologies are increasingly embedded in Neonatal Intensive Care Units (NICUs), yet the role of nurse-led governance in sh...

Development of a spatial radiomics nomogram for predicting unfavorable outcomes after acute ischemic stroke: A multicenter study.

OBJECTIVE: To develop and externally validate a spatial radiomics nomogram integrating infarct location, lesion radiomics, and clinical predictors for...

Deep learning integration of initial abdominal radiography and early clinical information for predicting hospitalization of patients with abdominal symptoms.

Abdominal symptoms are a common reason for emergency department visits, yet early admission decisions remain challenging due to limited diagnostic inf...

Prognostic value of preoperative CT-derived fractional flow reserve after transcatheter or surgical aortic valve replacement in patients with severe aortic stenosis.

BACKGROUND: Patients with aortic stenosis (AS) often have concomitant coronary artery disease (CAD), and coronary CTA (CCTA) is performed for anatomic...

Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.

INTRODUCTION: Recurrent psychiatric hospitalizations in youth are costly and burdensome to patients and families. While postdischarge planning rarely ...

Hidden pathways of antimicrobial resistance: A review of environmental metagenomics and exposure risks in low-resource settings.

Antimicrobial resistance (AMR) is increasingly recognised as a One Health challenge in which environmental reservoirs play an important role in the pe...

A predictive tool incorporating frailty scores for perioperative risk assessment in patients with spinal metastasis: a national retrospective cohort study.

BACKGROUND: Surgical treatment for spinal metastases is associated with high perioperative risk due to tumor burden, neurologic compromise, and limite...

A multicenter machine learning model for predicting ICU mortality in mechanically ventilated patients: development and external validation.

BACKGROUND: Accurate early prediction of mortality in mechanically ventilated intensive care unit (ICU) patients remains challenging due to disease he...

Development and multicenter validation of machine learning models for 28-day mortality in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common critical illness in the intensive care unit (ICU) and is ...

Improving Delirium Prediction in Surgery: Enhanced Machine Learning Models vs. Traditional Risk Scores.

BACKGROUND: Postoperative delirium (POD) is a prevalent complication among older surgical patients; however, accurately predicting its occurrence usin...