Critical Care

Clinical Predictive Models for COVID-19: Systematic Study.

BACKGROUND: COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on...

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.

A model that predicts levels of coronavirus (CoV) respiratory and fecal-oral transmission potentials based on the shell disorder has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Using primarily the PID of N, SARS-CoV-2 is grouped as havi...

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs...

Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

BACKGROUND: Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this stud...

Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.

Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially ...

AI in the Intensive Care Unit: Up-to-Date Review.

AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential lies in its ability to process large volumes of dat...

Acute kidney disease and long-term outcomes in critically ill acute kidney injury patients with sepsis: a cohort analysis.

BACKGROUND: Acute kidney injury (AKI) is frequent during hospitalization and may contribute to adverse short- and long-term consequences. Acute kidney...

A deep learning solution to recommend laboratory reduction strategies in ICU.

OBJECTIVE: To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial...

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to...

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critic...

Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks.

To improve the prediction accuracy of respiratory signals by adapting the multi-layer perceptron neural network (MLP-NN) model to changing respiratory...

Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers.

BACKGROUND: To introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on the fusion of multi-biomarker data; glucose, lacta...

Supervised classification techniques for prediction of mortality in adult patients with sepsis.

BACKGROUND: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may increase the accuracy for clinical decisions.

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals.

Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUR...

Data-driven ICU management: Using Big Data and algorithms to improve outcomes.

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" c...

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respirato...

Machine learning based refined differential gene expression analysis of pediatric sepsis.

BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biolo...

Evaluation of Hemodialysis Arteriovenous Bruit by Deep Learning.

Physical findings of auscultation cannot be quantified at the arteriovenous fistula examination site during daily dialysis treatment. Consequently, mi...

A Single-Center Evaluation of Extended Infusion Piperacillin/Tazobactam for Empiric Treatment in the Intensive Care Unit.

Piperacillin/tazobactam (PTZ) extended infusion (EI) is often used empirically in the intensive care unit (ICU). Gram-negative (GN) organisms with PT...