Intensivists

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke...

A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets.

Understanding the chemical-disease relations (CDR) is a crucial task in various biomedical domains. ...

A deep learning framework for pancreas segmentation with multi-atlas registration and 3D level-set.

In this paper, we propose and validate a deep learning framework that incorporates both multi-atlas ...

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United State...

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With i...

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans.

We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and C...

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm.

In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such ...

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanic...

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomi...

Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis.

The ultimate goal of machine learning-based myoelectric control is simultaneous and independent cont...

Automatic multi-needle localization in ultrasound images using large margin mask RCNN for ultrasound-guided prostate brachytherapy.

Multi-needle localization in ultrasound (US) images is a crucial step of treatment planning for US-g...

Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis.

The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and en...

Not All Sepsis-Associated Acute Kidney Injury Is the Same: There May Be an App for That.

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agn...

Clinical Predictive Models for COVID-19: Systematic Study.

BACKGROUND: COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapi...

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death...

Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.

OBJECTIVE: Currently, a major limitation for natural language processing (NLP) analyses in clinical ...

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.

Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide signi...

Metabolic pathway inference using multi-label classification with rich pathway features.

Metabolic inference from genomic sequence information is a necessary step in determining the capacit...

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

BACKGROUND: Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the i...

AI in the Intensive Care Unit: Up-to-Date Review.

AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential l...