Hospital-Based Medicine

Intensivists

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

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Showing 316-336 of 6,135 articles
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...

Host Biomarkers and Antibiotic Tissue Penetration in Sepsis: Insights from Moxifloxacin.

BACKGROUND AND OBJECTIVE: Sepsis-induced pathophysiological changes may lead to pharmacokinetic vari...

Early prediction of shock in intensive care unit patients by machine learning using discrete electronic health record data.

PURPOSE: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) pa...

[Acute respiratory distress syndrome-quo vadis : Innovative and individualized treatment approaches].

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome characterized by var...

Machine learning modeling and multi objective optimization of artificial detrusor.

To address the problem of obtaining optimal design parameters for existing artificial detrusors usin...

Fine-tune language models as multi-modal differential equation solvers.

In the growing domain of scientific machine learning, in-context operator learning has shown notable...

Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data.

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultu...

Heuristically enhanced multi-head attention based recurrent neural network for denial of wallet attacks detection on serverless computing environment.

Denial of Wallet (DoW) attacks are a cyber threat designed to utilize and deplete an organization's ...

Lightweight Multi-Stage Aggregation Transformer for robust medical image segmentation.

Capturing rich multi-scale features is essential to address complex variations in medical image segm...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic....

TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.

It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expres...

Multi-stage network for single image deblurring based on dual-domain window mamba.

Multi-stage methods have been proven effective and widely used in image deblurring research. These m...

CRISP: A causal relationships-guided deep learning framework for advanced ICU mortality prediction.

BACKGROUND: Mortality prediction is critical in clinical care, particularly in intensive care units ...

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