Critical Care

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.

Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this stud...

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation.

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention an...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).MethodsT...

Deep learning method with integrated invertible wavelet scattering for improving the quality ofcardiac DTI.

Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitations ofcardiac diffusion tensor imaging (DTI). We p...

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly as...

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires acc...

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials....

A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a...

Early prognosis prediction for non-variceal upper gastrointestinal bleeding in the intensive care unit: based on interpretable machine learning.

INTRODUCTION: This study aims to construct a mortality prediction model for patients with non-variceal upper gastrointestinal bleeding (NVUGIB) in the...

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate pr...

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable ...

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

OBJECTIVE: To explore the differences and associations of hypoxic parameters among distinct types of respiratory events in patients with obstructive s...

Multimodal fusion network for ICU patient outcome prediction.

Over the past decades, massive Electronic Health Records (EHRs) have been accumulated in Intensive Care Unit (ICU) and many other healthcare scenarios...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorde...

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retro...

A Novel Deep Learning Model for Breast Tumor Ultrasound Image Classification with Lesion Region Perception.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagno...

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm.

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS...

How artificial intelligence is transforming nephrology.

Current research in nephrology is increasingly focused on elucidating the complexity inherent in tightly interwoven molecular systems and their correl...

Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung...

Identification of gene and protein signatures associated with long-term effects of COVID-19 on the immune system after patient recovery by analyzing single-cell multi-omics data using a machine learning approach.

Viral infections significantly impact the immune system, and impact will persist until recovery. However, the influence of severe acute respiratory sy...