Critical Care

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from...

MNAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images. The network architect...

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer.

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed ...

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence.

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechani...

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds.

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is tr...

A deep learning system for heart failure mortality prediction.

Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates of prognosis HF patients are highly variable, rang...

Transvesical Percutaneous Access Allows for Epidural Anesthesia Without Mechanical Ventilation in Single-Port Robotic Radical and Simple Prostatectomy.

OBJECTIVES: To determine the feasibility of epidural anesthesia in patients undergoing transvesical single-port (SP) robotic simple and radical prosta...

Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone.

In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spont...

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.

The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black...

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in toda...

Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.

OBJECTIVES: To predict impending delirium in ICU patients using recurrent deep learning.

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning.

Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health ass...

LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

PURPOSE: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensiona...

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious ...

Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.

To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairme...

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.

OBJECTIVE: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) int...

Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane.

The detection and quantification of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus particles in ambient waters using a membrane-ba...

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation.

One of the major threats to marine ecosystems is pollution, particularly, that associated with the offshore oil and gas industry. Oil spills occur in ...

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning he...

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?