Hospital-Based Medicine

Intensivists

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

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Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation.

Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver h...

Developing a prediction model for cognitive impairment in older adults following critical illness.

BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an...

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Integrating diverse types of biological data is essential for a holistic understanding of cancer bio...

Analysis and actuation design of a novel at-scale 3-DOF biomimetic flapping-wing mechanism inspired by flying insects.

Insects' flight is imbued with endless mysteries, offering valuable inspiration to the flapping-wing...

Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion.

Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer ...

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit...

Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.

Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced ra...

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.

Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation o...

Unlocking biological complexity: the role of machine learning in integrative multi-omics.

The increasing complexity of biological systems demands advanced analytical approaches to decode the...

Precision Opioid Prescription in ICU Surgery: Insights from an Interpretable Deep Learning Framework.

PURPOSE: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical pa...

"Several birds with one stone": exploring the potential of AI methods for multi-target drug design.

Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies...

Pulmonary Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.

RATIONALE AND OBJECTIVES: Hyperpolarized Xe MRI quantifies ventilation-defect-percent (VDP), the rat...

Multi-task magnetic resonance imaging reconstruction using meta-learning.

Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquire...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomog...

A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients.

Sepsis, a life-threatening condition triggered by the body's response to infection, remains a signif...

Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due ...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, re...

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