AIMC Topic: Critical Illness

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Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.

BioMed research international May 30, 2022
To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is prim...
Humans Machine Learning Enteral Nutrition Critical Illness Logistic Models Critical Care
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Use of Robots in Critical Care: Systematic Review.

Journal of medical Internet research May 16, 2022
BACKGROUND: The recent focus on the critical setting, especially with the COVID-19 pandemic, has highlighted the need for minimizing contact-based care and increasing robotic use. Robotics is a rising field in the context of health care, and we sough...
Humans Pandemics COVID-19 Critical Illness Robotics Critical Care
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External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients.

Journal of nephrology May 12, 2022
OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients.
Critical Illness Intensive Care Units Deep Learning Oliguria Humans Acute Kidney Injury
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Machine learning-based prediction of critical illness in children visiting the emergency department.

PloS one Feb 17, 2022
OBJECTIVES: Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have ...
Child ROC Curve Triage Critical Illness Humans Emergency Service, Hospital Cross-Sectional Studies Republic of Korea Machine Learning Male Female Databases, Factual Infant Hospitalization Child, Preschool
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SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

PLoS computational biology Dec 21, 2021
Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxe...
Deep Learning Hypoxia Oxygen Saturation COVID-19 Critical Illness Intensive Care Units SARS-CoV-2 Humans Pandemics
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Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department.

Scientific reports Dec 15, 2021
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers bette...
Male Middle Aged ROC Curve Triage Female Aged Adult Odds Ratio Disease Susceptibility Area Under Curve Emergency Service, Hospital Critical Care Disease Management Humans Machine Learning Algorithms Critical Illness Clinical Decision-Making
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Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.

Scientific reports Nov 2, 2021
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of em...
Patient Readmission Humans Adult Machine Learning Male Risk Factors Middle Aged Triage Hospitalization Algorithms Adolescent Emergency Service, Hospital Aged Retrospective Studies Critical Illness Aftercare Young Adult Electronic Health Records Patient Discharge Female
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Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning.

Journal of healthcare engineering Oct 27, 2021
Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce th...
Humans Lung Neural Networks, Computer Deep Learning Critical Illness Ultrasonography
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Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.

Nutrients Oct 26, 2021
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...
Predictive Value of Tests Energy Metabolism Anthropometry Rest Algorithms Male Prospective Studies Nutrition Assessment Retrospective Studies Child, Preschool Critical Illness Child Humans Female Neural Networks, Computer Reproducibility of Results Cross-Sectional Studies
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eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

PloS one Sep 24, 2021
We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected f...
Adolescent Respiratory Distress Syndrome Oxygen Models, Biological Adult Medical Records Systems, Computerized Machine Learning SARS-CoV-2 Male Humans Respiratory Rate COVID-19 Risk Factors Middle Aged Critical Illness Aged Female Aged, 80 and over
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