Critical Care

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural complications, which can increase healthcare costs. Developing efficient strategies for resource allocation has be...

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and image analysis to address these limitations. Here, defects of four types- chipping, breaking, color non-uniformity and speckling, were manually induced in...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification.

While self-labeled methods can exploit unlabeled and labeled instances to train classifiers, they are also restricted by the labeled instance number a...

Interpretable machine learning models for COPD ease of breathing estimation.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitorin...

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration p...

Multi-center brain age prediction via dual-modality fusion convolutional network.

Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric ...

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.

BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. T...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting millions, costing billions, and claiming nearly 2 mil...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by c...

Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.

OBJECTIVE: To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRR...

Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.

BACKGROUND: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity...

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Inten...

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. De...

Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with seps...

DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.

INTRODUCTION: Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). However, there is still a lack of effecti...

Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge.

Machine learning is widely used in dentistry nowadays, offering efficient solutions for diagnosing dental diseases, such as periodontitis and gingivit...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators a...