Hospitalists

Impact of the COVID-19 pandemic on ischemic stroke hospitalizations and in-hospital outcomes in the United States: A survey-weighted cohort with predictive modeling.

INTRODUCTION: Ischemic stroke remains a leading cause of death in the United States, with the COVID-19 pandemic exacerbating disparities. Prior studies have been limited by small sample sizes and lack of generalizability. We used nationally representative data to assess the effects of COVID-19 on ischemic stroke care, outcomes, and predictive modeling. METHODS: We performed a retrospective cohort ...

A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.

OBJECTIVE: Based on multicenter clinical data, this study aimed to develop and validate a predictive model for chronic low back pain (CLBP) after lumbar decompression surgery in patients with diabetes mellitus and lumbar disc herniation. The model integrated metabolic indicators and imaging-derived features of the paraspinal muscles. METHODS: This study was designed as a multicenter retrospective ...

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Emergency department (ED) triage of older adults is challenging because standard early warning scores are often insensitive to atypical presentations....

Machine learning prediction of increased healthcare utilization in colectomy for inflammatory bowel disease.

PURPOSE: Healthcare spending for patients with inflammatory bowel disease (IBD) has steadily increased over the past few decades. While some of the in...

GLORIF1, a global river flow dataset created by integrating process-based modelling and machine learning.

Accurate streamflow data are essential for managing water resources and enhancing climate resilience worldwide. Yet, existing global streamflow datase...

Association between glucose-to-platelet ratio and all-cause mortality in critically ill patients with acute myocardial infarction: a retrospective cohort study.

BACKGROUND: Acute myocardial infarction (AMI) in critically ill patients is associated with high mortality. The glucose-to-platelet ratio (GPR), deriv...

Machine Learning-Based Predictive Model for Functional Independence in Spinal Cord Injury: Protocol for a Predictive Rule Development and Validation Study.

BACKGROUND: Spinal cord injury (SCI) causes substantial disability by disrupting spinal pathways, making functional independence a central rehabilitat...

Refined AI-ASPECTS with modified atlas and lesion-load thresholds: advancing acute ischemic stroke imaging and prognostic prediction.

BACKGROUND: The artificial intelligence-assisted ASPECTS (AI-ASPECTS) system has become an increasingly common tool in clinical practice for assessing...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To ...

Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.

BACKGROUND: Unstructured clinical text remains a major barrier to interoperable data reuse and large-scale secondary analysis in health care. Large la...

A physics-informed neural network framework for plant-wide prediction and dynamic control of wastewater treatment.

Mechanistic models and machine learning methods provide powerful capabilities for simulating and controlling wastewater treatment processes; however, ...

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PURPOSE: We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key pre...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model.

BACKGROUND: Prognostic assessment in critically ill cancer patients is challenging due to the suboptimal performance of traditional severity scores. W...

Machine learning phenotypes and heterogeneous albumin effects in cirrhotic AKI: a causal inference study.

Current guidelines recommend albumin infusion as a first-line treatment for acute kidney injury (AKI) in patients with cirrhosis. However, recent larg...

Inpatient Robotic Therapy for Stroke Survivors With Severe Upper Limb Impairments: A Randomized Controlled Trial.

IMPORTANCE: Understanding how upper extremity (UE) robotic therapy (RT) affects efficiency and effectiveness of inpatient rehabilitation is important ...

Machine learning-based prediction of ICU admission and mortality in Crimean-Congo hemorrhagic fever by using wide-range targeted metabolomics.

PURPOSE: This study aimed to evaluate the potential of amino-acid profiles to predict disease progression in patients with Crimean-Congo Hemorrhagic F...

Unravelling emergency department triage in everyday practice: An ethnographic study of underlying mechanisms across three Danish emergency departments.

BACKGROUND: Emergency department triage is commonly conceptualised as a standardised classification of patient urgency based on vital signs and presen...

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BACKGROUND: The objective of this study was to evaluate the performance of multiple machine learning algorithms to provide evidence supporting early i...

The HLH-Risk-Calculator is a machine learning-based tool to predict course & mortality of secondary hemophagocytic lymphohistiocytosis.

PURPOSE: Secondary hemophagocytic lymphohistiocytosis (sHLH) is a life-threatening hyperinflammatory condition. While few diagnostic scores are establ...

Machine Learning-Guided High-Efficiency and Thermally Stable Capacitive Energy Storage in Dielectric Capacitors With a Simple Chemical Composition.

Dielectric capacitors offering ultrafast charge-discharge capability and superior reliability are essential for advanced electronic systems, but achie...