Hospitalists

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 considerable heterogeneity, presenting substantial challenges for clinicians in establishing realistic goals. While integrative AI paradigms have shown success in other complex medical domains, existing models often rely on single-modality data, limiting...

Re-imagining discharge summary training through artificial intelligence.

What is the educational challenge? Discharge summary (DS) writing is a core competency for junior physicians, yet persistent deficiencies in the quality, accuracy, and timeliness of these clinical documents are well-documented, with downstream repercussions in patient safety and continuity of care. Existing educational interventions rely heavily on faculty-intensive, small-group teaching models, w...

Predicting wire electrical discharge machined surface roughness of C355/silicon nitride/graphene hybrid nanocomposites using simulation, statistical and machine learning techniques.

This study employed machine learning (ML) and optimization approaches, with support vector regression (SVR), artificial neural networks (ANNs) simulat...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations.

BACKGROUND: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and h...

Multi-scale kernel and electrode attention network for EEG-based epileptic seizure detection.

Epilepsy is a common chronic neurological disorder, and automated detection of epileptic seizures using multi-channel electroencephalography (EEG) is ...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

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BACKGROUND AND AIM: Over recent decades, formal requirements for medical records have been strengthened, for example through patients' rights of acces...

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion r...

Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

OBJECTIVE: Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential rol...

Predicting Geriatric Rehabilitation Stays of ≤4 Weeks After Hip Fracture Surgery: Machine Learning Approach Using Physical Activity and Patient Data.

BACKGROUND: In 2022, over 18,000 patients aged ≥70 years were hospitalized in the Netherlands for a hip fracture, with 50% requiring geriatric rehabil...

Machine learning-based prediction of disability in subacute low back pain: a primary care study on clinical and psychosocial determinants.

BACKGROUND: Subacute low back pain (LBP) is a highly prevalent condition and a major contributor to disability and health care burden. Early identific...

Prediction of COVID-19 hospitalisation, ICU admission or death following ChAdOx1 vaccination using artificial intelligence: A clinical predictive model from the English RAVEN study.

OBJECTIVES: This study identifies predictors of severe COVID-19 following completion of two-dose primary series of the AZD1222 COVID-19 vaccine, emplo...

Automated Detection of Ventilator Asynchronies: A Clinical and Technological Perspective.

Patient-ventilator asynchrony is highly prevalent during invasive mechanical ventilation, yet its detection at the bedside remains limited. Convention...

Interpretable machine learning and deep neural networks for ICU admission prediction in paediatric respiratory patients.

Prediction of Major Stroke (NIHSS > 5) Based on MR Perfusion Imaging Using a 3D ResNet.

PURPOSE: Prediction of stroke symptoms from perfusion patterns in ischemic stroke imaging would be desirable for decision making in relevant clinical ...

Development and validation of predictive models for prognostic assessment in patients with acute non-ST-segment elevation myocardial infarction: a multicentre real-world study from China.

BACKGROUND: Few models specifically predict the prognoses of non-ST-segment elevation myocardial infarction (NSTEMI) patients. Additionally, these mod...

Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification re...

Association between monocyte-to-lymphocyte ratio and mortality in patients with acute pancreatitis requiring intensive care unit admission: a retrospective cohort study and predictive model establishment based on machine learning.

The purpose of this study was to evaluate the predictive value of monocyte-to-lymphocyte ratio (MLR) on the short-term (28 days) and long-term (365 da...

A machine learning model to identify pulmonary embolism in patients admitted to intensive care.

BACKGROUND: Pulmonary embolism (PE) is a leading cause of preventable death, yet statistical prediction models have shown inconsistent validity. Our p...