Intensivists

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics.

Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose...

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

OBJECTIVE: As data science and artificial intelligence continue to rapidly gain traction, the public...

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 ...

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 s...

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 ...

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 mac...

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 activ...

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 o...

Transportability and Implementation Challenges of Early Warning Scores for Septic Shock in the ICU: A Perspective on the TREWScore.

The increased use of electronic health records (EHRs) has improved the availability of routine care ...

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 acc...

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.

OBJECTIVES: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive...

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

The study aimed to establish a machine learning-based scoring nomogram for early recognition of like...

Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatm...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus dise...

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data.

The ability to perform accurate prognosis is crucial for proactive clinical decision making, informe...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hos...

An explainable machine learning framework for lung cancer hospital length of stay prediction.

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using mach...

Bringing biological ARDS phenotypes to the bedside with machine-learning-based classifiers.

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and ef...

I, DOCTOR: The role of machine learning in phenotyping ARDS.

Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma).

BACKGROUND: Critical care research in Canada is conducted primarily in academically-affiliated inten...