Critical Care

Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the use of respiratory signal quality quantification and several neural network (NN) structures for improved RR estimation. We extract respiratory modulati...

Identification of pediatric respiratory diseases using a fine-grained diagnosis system.

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in pr...

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings.

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of br...

A Survey on Multi-View Clustering.

Clustering is a machine learning paradigm of dividing sample subjects into a number of groups such that subjects in the same groups are more similar t...

Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiv...

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 (allo-HCT), and timely detection is crucial. Howe...

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality ...

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the deg...

Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological...

Safety-driven design of machine learning for sepsis treatment.

Machine learning (ML) has the potential to bring significant clinical benefits. However, there are patient safety challenges in introducing ML in comp...

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 have focused exclusively on single-label waste clas...

Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis.

INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper eval...

IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control.

Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-intersection traffic signal control. The MDRL algorithms produce the d...

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and...

Integrated multi-omics analysis of ovarian cancer using variational autoencoders.

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics ana...

Socially Assistive Robots in Aged Care: Ethical Orientations Beyond the Care-Romantic and Technology-Deterministic Gaze.

Socially Assistive Robots (SARs) are increasingly conceived as applicable tools to be used in aged care. However, the use carries many negative and po...

Generalized and transferable patient language representation for phenotyping with limited data.

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this w...

The effect of diabetes on major robotic hepatectomy.

Studies regarding the influence of diabetes on perioperative outcomes after major hepatectomy are conflicting. The objective of this study is to analy...

Sugammadex versus neostigmine on postoperative pulmonary complications after robot-assisted laparoscopic prostatectomy: a propensity score-matched analysis.

PURPOSE: Robot-assisted laparoscopic prostatectomy (RALP) requires particular surgical conditions, such as carbon dioxide pneumoperitoneum and steep T...

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery.

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventila...