Intensivists

Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-making by evaluating the performance of various supervised and unsupervised models.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU).

Semi-supervised multi-modal medical image segmentation with unified translation.

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervis...

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in t...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, ...

A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing.

OBJECTIVE: To integrate an enhanced molecular diagnostic technique to develop and validate a machine-learning model for diagnosing sepsis.

Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP.

BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infec...

Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm.

Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, ...

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk...

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently...

Social Workers' Perspectives on Socially Isolated Older Adults Living with a Robot Companion.

Addressing depression and social isolation among solo-living older adults in South Korea requires a multi-faceted approach. This study aims to explore...

Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential ...

How artificial intelligence could transform emergency care.

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decisio...

The application of multi-scale simulation in advanced electronic packaging.

Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems fro...

Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control.

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complex...

MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule.

Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-cl...

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. T...

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for pr...

DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions.

Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic ...

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia...