Critical Care

Construction of a multi-modal digital human education platform based on GAN and vision transformer.

With the rapid development of artificial intelligence technology, digital human education platforms ...

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes a...

All-Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi-State Memory with Quantization for Efficient Neural Networks.

The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory d...

OPTUNA optimization for predicting chemical respiratory toxicity using ML models.

Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly...

Layer Frozen Multi-Net & Latent Space Feature-Concealed Backdoor Samples Detection.

Identifying feature-concealed backdoor samples that entangle with benign semantics of target-class o...

An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography.

BACKGROUND: Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and d...

A multi-site study of clinician perspectives in the lifecycle of an algorithmic risk prediction tool.

Recent advancements in the performative capacities of artificial intelligence (AI), machine learning...

Machine learning-based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker.

BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challen...

Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with inc...

Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

Long-term power load forecasting is critical for power system planning but is constrained by intrica...

Deep learning for fetal inflammatory response diagnosis in the umbilical cord.

INTRODUCTION: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine i...

Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.

BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable m...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impac...

Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis.

Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Urbanization and industrialization have led to widespread soil heavy metals contamination, posing si...

Beyond immunosuppression: decoding systemic immune dysregulation in ICU-acquired pneumonia.

Timing of kidney replacement therapy in critically ill patients: A call to shift the paradigm in the era of artificial intelligence.

Acute kidney injury (AKI) is a common condition in intensive care units (ICUs) and is associated wit...

Saccade and purify: Task adapted multi-view feature calibration network for few shot learning.

Current few-shot image classification methods encounter challenges in extracting multi-view features...

IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. ...

Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray ...

Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict t...