Hospital-Based Medicine

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

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Showing 1807-1827 of 6,177 articles
Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administ...

A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors.

People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as epis...

Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction.

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are both non-coding RNAs (ncRNAs) and their in...

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or...

Adaptive Floor Cleaning Strategy by Human Density Surveillance Mapping with a Reconfigurable Multi-Purpose Service Robot.

Professional cleaning and safe social distance monitoring are often considered as demanding, time-co...

A dual-stream deep attractor network with multi-domain learning for speech dereverberation and separation.

Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker ...

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical i...

Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review.

BACKGROUND: Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and elec...

Somatosensitive film soft crawling robots driven by artificial muscle for load carrying and multi-terrain locomotion.

Somatosensitive soft crawling robotics is highly desired for load carrying and multi-terrain locomot...

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

BACKGROUND AND OBJECTIVES: Sepsis is a life-threatening condition that is associated with increased ...

Multi-label classification and label dependence in in silico toxicity prediction.

Most computational predictive models are specifically trained for a single toxicity endpoint and lac...

A Survey on Multi-View Clustering.

Clustering is a machine learning paradigm of dividing sample subjects into a number of groups such t...

Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant...

Safety-driven design of machine learning for sepsis treatment.

Machine learning (ML) has the potential to bring significant clinical benefits. However, there are p...

A deep convolutional neural network to simultaneously localize and recognize waste types in images.

Accurate waste classification is key to successful waste management. However, most current studies h...

Full-length ribosome density prediction by a multi-input and multi-output model.

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eu...

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image class...

Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.

Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflo...

Predicting treatment response from longitudinal images using multi-task deep learning.

Radiographic imaging is routinely used to evaluate treatment response in solid tumors. Current imagi...

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