Intensivists

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are usef...

Effective allocation of resources in water pollution treatment alternatives: a multi-stage gray group decision-making method based on hesitant fuzzy linguistic term sets.

With the significant economic shift, water pollution treatment has gradually become a key problem wh...

Clinical management of sepsis can be improved by artificial intelligence: yes.

Correlation of Troponin Level (Troponin T, Troponin I) With PELOD-2 Score in Sepsis as a Predictive Factor of Mortality.

BACKGROUND: Sepsis in children with cardiovascular involvement can increase mortality. Recently, man...

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and fol...

Evaluation of Amikacin Pharmacokinetics in Critically Ill Patients with Intra-abdominal Sepsis.

Although the current widespread use of amikacin is in intra-abdominal sepsis treatment, its pharmac...

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental c...

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis.

Medical prediction is always collectively determined based on bioimages collected from different sou...

Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.

Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's n...

Usefulness of presepsin as diagnostic and prognostic marker of sepsis in daily clinical practice.

INTRODUCTION: Sepsis represents a major cause of morbidity and mortality in critically ill patients....

A scalable multi-signal approach for the parallelization of self-organizing neural networks.

Self-Organizing Neural Networks (SONNs) have a wide range of applications with massive computational...

Predicting O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neu...

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis.

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron...

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring.

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-t...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to d...

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning.

In in-silico prediction for molecular binding of human genomes, promising results have been demonstr...

Pulmonary Textures Classification via a Multi-Scale Attention Network.

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) ...

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in...

Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning.

Multi-indices quantification of optic nerve head (ONH), measuring ONH appearance with multiple types...

A Multi-Column CNN Model for Emotion Recognition from EEG Signals.

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep...

Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions.

UNLABELLED: To develop and evaluate a novel strategy that automates the retrospective identification...