Hospital-Based Medicine

Intensivists

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

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Showing 2521-2541 of 6,181 articles
multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information.

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, an...

Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.

OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model tha...

Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method i...

Self-supervised multi-modality learning for multi-label skin lesion classification.

BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical...

Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation.

Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recent...

Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation.

Recent advances in prototypical learning have shown remarkable potential to provide useful decision ...

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. ...

scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

Single-cell multi-omics sequencing technology comprehensively considers various molecular features t...

Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation.

Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it h...

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multip...

Multi-Gate Mixture of Multi-View Graph Contrastive Learning on Electronic Health Record.

Electronic Health Record (EHR) is the digital form of patient visits that contains various medical d...

MLOmics: Cancer Multi-Omics Database for Machine Learning.

Framing the investigation of diverse cancers as a machine learning problem has recently shown signif...

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.

BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue t...

A deep learning model for prediction of lysine crotonylation sites by fusing multi-features based on multi-head self-attention mechanism.

Lysine crotonylation (Kcr) is an important post-translational modification, which is present in both...

scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellu...

GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.

Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced sur...

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise...

Application of AI-assisted multi-advisor system combined with BOPPPS teaching model in clinical pharmacy education.

BACKGROUND: The development of clinical pharmacy in China has been relatively slow, and standardized...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays ...

ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered out...

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