Intensivists

Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification.

Part-level representations are important for robust person re-identification (ReID), but in practice...

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data.

Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods r...

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features.

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechani...

MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.

Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug d...

Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury.

This study was aimed at analyzing the diagnostic value of convolutional neural network models on acc...

Multi-Modal Song Mood Detection with Deep Learning.

The production and consumption of music in the contemporary era results in big data generation and c...

Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation.

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In th...

Pulmonary nodules detection based on multi-scale attention networks.

Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of ...

Instance segmentation convolutional neural network based on multi-scale attention mechanism.

Instance segmentation is more challenging and difficult than object detection and semantic segmentat...

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive...

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.

OBJECTIVES: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive...

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

The study aimed to establish a machine learning-based scoring nomogram for early recognition of like...

Robot System Assistant (RoSA): Towards Intuitive Multi-Modal and Multi-Device Human-Robot Interaction.

This paper presents an implementation of RoSA, a Robot System Assistant, for safe and intuitive huma...

Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatm...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus dise...

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications.

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated t...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hos...

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data.

The ability to perform accurate prognosis is crucial for proactive clinical decision making, informe...

Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars.

Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular...

An explainable machine learning framework for lung cancer hospital length of stay prediction.

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using mach...

Bringing biological ARDS phenotypes to the bedside with machine-learning-based classifiers.