Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Showing 43-63 of 6,135 articles
Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.

Cognitive impairment is a frequent complication of Parkinson's disease (PD), affecting up to half of...

Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.

BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in criti...

Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.

BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) ...

Touchless monitoring of neonatal activity: a multi-center study.

BACKGROUND: Neonatal activity level is an important physiological parameter linked to lethargic resp...

Artificially intelligent nasal perception for rapid sepsis diagnostics.

Sepsis, a life-threatening disease caused by infection, presents a major global health challenge due...

Vox-MMSD: Voxel-wise Multi-scale and Multi-modal Self-Distillation for Self-supervised Brain Tumor Segmentation.

Many deep learning methods have been proposed for brain tumor segmentation from multi-modal Magnetic...

A Semi-supervised Reinforcement Learning Framework Incorporating the Multi-scale IncepMambaNet Network for Glaucoma Progression Prediction.

Glaucoma is the leading cause of irreversible blindness worldwide. Currently, artificial intelligenc...

Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban environments.

The emerging density in today's urban environments requires a strong multi-camera architecture for r...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate...

Federated fault diagnosis method for collaborative self-diagnosis and cross-robot peer diagnosis.

In multi-robot collaboration, individual failures can propagate to other robots due to the topologic...

Dual-Wavelength Synaptic Simulator ReS/TaNiSe for Multi-Timescale Learning in Neuromorphic Computing.

To address the limitations of silicon-based devices in neuromorphic computing, this study proposes a...

Early detection of ICU-acquired infections using high-frequency electronic health record data.

BACKGROUND: Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier i...

Multi-scale feature fusion keypoint detection network for ship draft line localization.

In the maritime industry, accurately detecting a ship's draft line is crucial for ensuring transacti...

An illustration of multi-class roc analysis for predicting internet addiction among university students.

The internet is one of the essential tools today, and its impact is particularly felt among universi...

Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models.

OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) rema...

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction.

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis...

Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis.

Data-driven intelligent fault diagnosis methods have become essential for ensuring the reliability a...

A defense method against multi-label poisoning attacks in federated learning.

Federated learning is a distributed machine learning framework that allows multiple parties to colla...

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