Hospital-Based Medicine

Attitudes of nurses toward artificial intelligence: A multicenter comparison.

BackgroundArtificial intelligence (AI) is transforming medical practices with rapidly developing technologies and the innovative solutions it provides. In order for this transformation to be successfully integrated into healthcare services, healthcare professionals must have positive attitudes towards this technology.ObjectiveThe present study was conducted with the aim of comparing the attitudes ...

Developing a prediction model for cognitive impairment in older adults following critical illness.

BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-ICU cognitive impairment to guide delivery of screening and support resources to those in greatest need. We sought to develop and internally validate a...

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to...

External validation and performance analysis of a deep learning-based model for the detection of intracranial hemorrhage.

PurposeWe aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH...

Screening for frequent hospitalization risk among community-dwelling older adult between 2016 and 2023: machine learning-driven item selection, scoring system development, and prospective validation.

BACKGROUND: Screening for frequent hospitalizations in the community can help prevent super-utilizers from growing in the inpatient population. Howeve...

Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.

BACKGROUND: The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management...

Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research.

Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the ch...

Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequ...

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determ...

Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.

BACKGROUND: Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited c...

Interpretable machine learning for time-to-event prediction in medicine and healthcare.

Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthc...

Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic...

Performance of deep learning models in predicting the nugent score to diagnose bacterial vaginosis.

The Nugent score is a commonly used diagnostic tool for bacterial vaginosis. However, its accuracy depends on the skills of laboratory technicians. Th...

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on pre...

An interpretable machine learning scoring tool for estimating time to recurrence readmissions in stroke patients.

BACKGROUND: Stroke recurrence readmission poses an additional burden on both patients and healthcare systems. Risk stratification aims to accurately d...

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there...

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be ...

Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction...

Extracting social determinants of health from inpatient electronic medical records using natural language processing.

BACKGROUND: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract t...

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks...