Critical Care

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this paper, we evaluate the performance of deep learning models for respiratory rate prediction. We consider three datasets from bio-sensors which include ele...

The Feasibility of a Machine Learning Approach in Predicting Successful Ventilator Mode Shifting for Adult Patients in the Medical Intensive Care Unit.

: Traditional assessment of the readiness for the weaning from the mechanical ventilator (MV) needs respiratory parameters in a spontaneous breath. Exempted from the MV disconnecting and manual measurements of weaning parameters, a prediction model based on parameters from MV and electronic medical records (EMRs) may help the assessment before spontaneous breath trials. The study aimed to develop ...

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-1...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosi...

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means o...

Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume.

World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease 2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing d...

Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with machine l...

Prediction of Resuscitation for Pediatric Sepsis from Data Available at Triage.

Pediatric sepsis imposes a significant burden of morbidity and mortality among children. While the speedy application of existing supportive care meas...

Learning Predictive and Interpretable Timeseries Summaries from ICU Data.

Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk...

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records.

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imagi...

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVI...

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.

Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support syst...

Reintubation Summation Calculation: A Predictive Score for Extubation Failure in Critically Ill Patients.

OBJECTIVE: To derive and validate a multivariate risk score for the prediction of respiratory failure after extubation.

A machine learning model for predicting deterioration of COVID-19 inpatients.

The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding...

Early heart rate variability evaluation enables to predict ICU patients' outcome.

Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has be...

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

BACKGROUND: Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We ...

Safety and efficacy of a novel robotic transcranial doppler system in subarachnoid hemorrhage.

Delayed cerebral ischemia (DCI) secondary to vasospasm is a determinate of outcomes following non-traumatic subarachnoid hemorrhage (SAH). SAH patient...

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study.

Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scena...

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 account of deep learning for severe sepsis complicate...

Pulmonary nodules detection based on multi-scale attention networks.

Pulmonary nodules are the main manifestation of early lung cancer. Therefore, accurate detection of nodules in CT images is vital for lung cancer diag...