Hospital-Based Medicine

Intensivists

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

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Showing 2479-2499 of 6,181 articles
Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments.

State-of-the-art research of traditional computer vision is increasingly leveraged in the surgical d...

Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Whole slide images (WSI), due to their gigabyte-scale size and ultra-high resolution, play a signifi...

A systematic methodological evaluation of sepsis guidelines: Protocol for quality assessment and consistency of recommendations.

BACKGROUND: Sepsis is a leading cause of mortality worldwide, characterized by a dysregulated host r...

Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is impor...

Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging.

OBJECTIVES: Achieving efficient analysis of multiple pathological indicators has great significance ...

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

The prediction of Intensive Care Unit (ICU) readmission has become a crucial area of research due to...

GLOBAL TRENDS IN ARTIFICIAL INTELLIGENCE AND SEPSIS-RELATED RESEARCH: A BIBLIOMETRIC ANALYSIS.

Background: In the field of bibliometrics, although some studies have conducted literature reviews a...

Spatio-temporal crash severity analysis with cost-sensitive multi-graphs attention network.

Most conventional crash severity models attempt to achieve a low classification error rate, implicit...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with m...

Multi-level semantic-aware transformer for image captioning.

Effective visual representation is crucial for image captioning task. Among the existing methods, th...

CFI-Former: Efficient lane detection by multi-granularity perceptual query attention transformer.

Benefiting from the booming development of Transformer methods, the performance of lane detection ta...

Task-augmented cross-view imputation network for partial multi-view incomplete multi-label classification.

In real-world scenarios, multi-view multi-label learning often encounters the challenge of incomplet...

Attribute-guided feature fusion network with knowledge-inspired attention mechanism for multi-source remote sensing classification.

Land use and land cover (LULC) classification is a popular research area in remote sensing. The info...

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

Distributed machine learning in mobile adhoc networks faces significant challenges due to the limite...

CHRONIC CRITICAL ILLNESS IN BONE TRAUMA PATIENTS: AN AI-BASED APPROACH FOR INTENSIVE CARE UNIT HEALTHCARE PROVIDERS.

Background: Chronic critical illness (CCI) is a serious condition characterized by a prolonged cours...

Deciphering the Regulatory Networks of the Migrasome-Associated Cell Subpopulation in Heterotopic Ossification via Multi-Omics Analysis.

Heterotopic ossification (HO) is a pathological process where bone forms in extraskeletal tissues, o...

[Application of multi-scale spatiotemporal networks in physiological signal and facial action unit measurement].

Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological sign...

Primer on large language models: an educational overview for intensivists.

The integration of artificial intelligence (AI) and machine learning-enabled medical technologies in...

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Characterizing cancer presents a delicate challenge as it involves deciphering complex biological in...

Cluster discharge resonance neuron model and its application in machinery multi-dimensional fault vibration signals.

Through the analysis of multidimensional vibration signals of machinery, existing faults in mechanic...

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