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Critical Care

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Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data.

Studies in health technology and informatics
Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SI...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

The journal of trauma and acute care surgery
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive model...

Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review.

Hospital pediatrics
CONTEXT: Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children.

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.

Critical care clinics
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care un...

Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury.

The journal of trauma and acute care surgery
BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constraints, 55% is the greatest reported guidelines compliance rate in severe trauma patients. This study a...

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Pediatric emergency care
BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we develo...

Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions.

Current opinion in critical care
PURPOSE OF REVIEW: Patients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be aided by risk prediction. Although emerging artificial intelligence/machine learning (AI/ML) algorit...

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

Investigative radiology
OBJECTIVES: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patient...

A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.

Critical care medicine
OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated ...

Very Early Robot-Assisted Mobilization of Intensive Care Patients - A Scoping Review.

Studies in health technology and informatics
This scoping review gives an overview of current research activities in the field of very early mobilization with robotic devices of intensive care patients. It presents the effect of very early, robot-assisted mobilization on intensive care patients...