Hospitalists

Predicting activities of daily living at discharge in stroke patients using rehabilitation robot training-induced functional connectivity.

BACKGROUND: Predicting activities of daily living (ADL) in stroke patients optimizes discharge planning, which relies on accurate functional assessment. Recent studies have shown that functional connectivity (FC) of brain networks induced by upper extremity rehabilitation robotic training (UE-RAT) effectively reflects functional status, but its prognostic value for ADL remains unclear. OBJECTIVE: ...

Artificial intelligence applications for enhancing patient self-care education following sternotomy: Development and initial evaluation.

BACKGROUND: Research indicates that over 12% of patients undergoing coronary artery bypass grafting and more than 14% of patients undergoing surgical aortic valve replacement experience rehospitalization within 30 days of discharge. Many of these re-admissions are potentially preventable. Comprehensive discharge education is essential, and AI applications can deliver tailored recommendations that ...

Technology and Innovation in Care Transitions: Imagining the Future of Postdischarge Care.

Care transitions remain high-risk periods, with up to 28% of patients experiencing adverse events (AEs) or readmissions within 30 days of discharge. T...

The use of large language models in clinical documentation: A scoping review.

BACKGROUND: Clinical documentation is essential for safe, high-quality care but has become increasingly complex, contributing to clinician burnout. La...

Dataset of numerical assessment on the combined effects of non-thermal plasma and water addition in hydrogen combustion.

This dataset contains raw and analyzed numerical simulation data, tables, and figures focused on the synergistic effects of nanosecond repetitively pu...

Multi-data fusion and machine learning for predicting ballast water discharge in ports.

Although ballast water is essential for vessel stability, its discharge is a major vector for invasive species spread. With stricter regulations, accu...

Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequ...

Prediction of Postherpetic Neuralgia in Patients with Acute and Subacute Herpetic Neuralgia Using Structural Magnetic Resonance Imaging: A Retrospective Study.

INTRODUCTION: This study integrated structural magnetic resonance imaging (sMRI) of the brain with clinical characteristics to identify the "vulnerabl...

Synergistic integration of multidimensional differential spectroscopy and deep learning for robust phenol monitoring in complex industrial effluents.

BACKGROUND: Phenol is a key chemical intermediate and a common, toxic pollutant found in industrial wastewater from processes like coking and oil refi...

Trajectories in renal perfusion pressure during hemodynamically guided therapy are associated with worsening renal function and patient outcomes.

BACKGROUND: Worsening renal function (WRF) during acute decompensated heart failure (ADHF) therapy portends worse outcomes. We hypothesized that renal...

Degradation of organic contaminants by non-thermal plasma: Unraveling the pH-dependent mechanism based on experiment, density functional theory analysis and machine learning.

This paper studies the dependence of different pollutant degradation by dielectric barrier discharge plasma (DBD) technology on pH through experiments...

Benchmarking foundation models and parameter-efficient fine-tuning for prognosis prediction in medical imaging.

BACKGROUND AND OBJECTIVES: Despite the significant potential of Foundation Models (FMs) in medical imaging, their application to prognosis prediction ...

New insights into machine learning prediction techniques for real-time sanitary risk assessment in karst drinking water sources affected by faecal contamination.

Safe drinking water supply from karst aquifers faces several challenges due to their high vulnerability to contamination, which can result in abrupt w...

Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a natural language processing algorithm.

INTRODUCTION: Pressure injuries (PIs) are one of the most common hospital-acquired complications in patients admitted to an intensive care unit (ICU)....

Patient Safety During Transitions: Identifying and Mitigating Risks.

Transitions of care, such as intrahospital handoffs and hospital discharge, are high-risk periods for adverse events like medication errors and diagno...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Symptom trajectories and their predictive factors in psychiatric inpatients with depression.

BACKGROUND: Depression presents with heterogeneity in symptom trajectories, complicating individualized treatment. Identifying distinct classes of sym...

Explanation Beyond Individual Features: Instance-wise Feature Grouping for EHR Predictive Analytics.

Identifying relevant input features which contribute to the output of a clinical prediction model can enhance the model explainability. To allow the e...

Ambient AI reduces documentation time and enhances quality in a simulated inpatient setting.

BACKGROUND: Clinical documentation is essential for safe and effective patient care but places a considerable clerical burden on clinicians. Ambient a...

Toward transparent clinical decision support in the ICU: A multi-window, model-agnostic explainability approach for 72-h mortality prediction using the eICU collaborative research database.

OBJECTIVE: This study has two main objectives: (1) to develop a multi-model framework for predicting Intensive Care Unit (ICU) mortality within the fi...