Critical Care

DeepMPM: a mortality risk prediction model using longitudinal EHR data.

BACKGROUND: Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional mortality risk prediction methods can hardly extract the information in longitudinal electronic medical records (EHRs) effectively, since they simply aggregate the heterogeneous variables in EHRs, ignoring the complex relationship and interactions be...

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

OBJECTIVES: To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients.

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a c...

Early prediction of hemodialysis complications employing ensemble techniques.

BACKGROUND AND OBJECTIVES: Hemodialysis complications remain a critical threat among dialysis patients. They result in sudden termination of the sessi...

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumber...

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respir...

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.

The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Co...

Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization.

In order to establish the mapping relationship between architectural design parameters and building performance and optimize architectural design para...

Predicting respiratory motion using a novel patient specific dual deep recurrent neural networks.

Real-time tracking of a target volume is a promising solution for reducing the planning margins and both dosimetric and geometric uncertainties in the...

A novel MCF-Net: Multi-level context fusion network for 2D medical image segmentation.

Medical image segmentation is a crucial step in the clinical applications for diagnosis and analysis of some diseases. U-Net-based convolution neural ...

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction mo...

Multi-Agent Multi-View Collaborative Perception Based on Semi-Supervised Online Evolutive Learning.

In the edge intelligence environment, multiple sensing devices perceive and recognize the current scene in real time to provide specific user services...

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' progn...

Occupational health and safety risk assessment using a fuzzy multi-criteria approach in a hospital in Chennai, India.

Hospitals provide direct and indirect employment benefits to medical professionals. Accidents in hospitals often lead to disastrous consequences such...

Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism.

Commonly used nested entity recognition methods are span-based entity recognition methods, which focus on learning the head and tail representations o...

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in thi...

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.

Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold stan...

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (S...

Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability.

The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This pa...