Hospital-Based Medicine

Intensivists

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

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Multi-Objective Evolutionary Optimization Boosted Deep Neural Networks for Few-Shot Medical Segmentation With Noisy Labels.

Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentatio...

Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.

IMPORTANCE: Sepsis is a major cause of morbidity and mortality, with early intervention shown to imp...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, v...

Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.

OBJECTIVES: Hospitalized community-acquired pneumonia (CAP) patients are admitted for ventilation, v...

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottl...

GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.

Accurate identification of angiotensin-I-converting enzyme (ACE) inhibitory peptides is essential fo...

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not ...

Rapid identification of coffee species and origin using affordable multi-channel spectral sensor combined with machine learning.

The rapid identification of coffee species and origin is critical for ensuring quality control and a...

Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model.

BACKGROUND: Acute kidney injury (AKI) is a critical complication in intensive care units (ICUs) that...

Prognostic value of the Glucose-to-Albumin ratio in sepsis-related mortality: A retrospective ICU study.

AIMS: To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day...

Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.

Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while ...

MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.

Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels with...

Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascul...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage r...

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