Hospital-Based Medicine

Intensivists

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

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Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains cha...

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots.

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positive...

Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.

Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is...

Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.

Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-v...

Multi-label zero-shot human action recognition via joint latent ranking embedding.

Human action recognition is one of the most challenging tasks in computer vision. Most of the existi...

Effect of dexmedetomidine sedation on swallowing reflex: A pilot study.

BACKGROUND/PURPOSE: Swallowing reflex depression during dental treatment or oral surgery may cause w...

Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification.

Multi-label classification studies the task where each example belongs to multiple labels simultaneo...

Randomised Comparison between the Efficacy of Two Doses of Nebulised Dexmedetomidine for Premedication in Paediatric Patients.

OBJECTIVE: Nebulised dexmedetomidine can be an easy alternative for preoperative sedation in paediat...

Sepsis in Latent Autoimmune Diabetes in Adults with Diabetic Ketoacidosis: A Case Report.

BACKGROUND: This case report intends to highlight the challenge in diagnosing type 1 diabetes on an ...

Privacy-enhanced multi-party deep learning.

In multi-party deep learning, multiple participants jointly train a deep learning model through a ce...

DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.

Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient ...

ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU...

Clinical applications of artificial intelligence in sepsis: A narrative review.

Many studies have been published on a variety of clinical applications of artificial intelligence (A...

Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network.

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatm...

Adaptive latent similarity learning for multi-view clustering.

Most existing clustering methods employ the original multi-view data as input to learn the similarit...

Non-faradaic electrochemical impedimetric profiling of procalcitonin and C-reactive protein as a dual marker biosensor for early sepsis detection.

In this work, we demonstrate a robust, dual marker, biosensing strategy for specific and sensitive e...

Endocan serum concentration in uninfected newborn infants.

INTRODUCTION: Endocan is a specific endothelial mediator involved in the inflammatory response. Its ...

Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection.

CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives...

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of anti...

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