AIMC Topic: Intensive Care Units

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Validation of a Machine Learning Model for Early Shock Detection.

Military medicine Jan 4, 2022
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...
Humans Machine Learning Prospective Studies Adolescent ROC Curve Vital Signs Retrospective Studies Intensive Care Units
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Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.

Critical care clinics Jan 1, 2022
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...
Decision Support Systems, Clinical Critical Care Machine Learning Intensive Care Units Humans Artificial Intelligence
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[Predicting prolonged length of intensive care unit stay machine learning].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences Dec 18, 2021
OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and...
Humans Machine Learning Aged Male Middle Aged Research Design Intensive Care Units
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Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury.

Current opinion in critical care Dec 1, 2021
PURPOSE OF REVIEW: Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of th...
Deep Learning Humans Artificial Intelligence Acute Kidney Injury Critical Illness Intensive Care Units
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An Interpretable Intensive Care Unit Mortality Risk Calculator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Mortality risk is a major concern to patients who have just been discharged from the intensive care unit (ICU). Many studies have been directed to construct machine learning models to predict such risk. Although these models are highly accurate, they...
Intensive Care Units Logistic Models Machine Learning Databases, Factual Humans Neural Networks, Computer
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Detecting Uncertainty of Mortality Prediction Using Confident Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Early mortality prediction is an actively researched problem that has led to the development of various severity scores and machine learning (ML) models for accurate and reliable detection of mortality in severely ill patients staying in intensive ca...
Uncertainty Intensive Care Units Hospital Mortality Machine Learning Databases, Factual Humans
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Predicting brain function status changes in critically ill patients via Machine learning.

Journal of the American Medical Informatics Association : JAMIA Oct 12, 2021
OBJECTIVE: In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hosp...
Prospective Studies Humans Brain Intensive Care Units Machine Learning Critical Illness Adult
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Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.

Medicine and science in sports and exercise Sep 1, 2021
PURPOSE: Rhabdomyolysis (RM) is a complex set of clinical syndromes that involves the rapid dissolution of skeletal muscles. Mortality from RM is approximately 10%. This study aimed to develop an interpretable and generalizable model for early mortal...
Female Aged Male Middle Aged Adult Machine Learning Retrospective Studies Intensive Care Units Humans Rhabdomyolysis Hospital Mortality Predictive Value of Tests
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Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

Journal of the American Medical Informatics Association : JAMIA Aug 13, 2021
OBJECTIVE: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - i...
Multiple Organ Failure Humans Intensive Care Units Machine Learning Neural Networks, Computer Electronic Health Records
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Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.) Jul 1, 2021
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...
Vasoconstrictor Agents Vital Signs Female Needs Assessment Aged Cohort Studies Critical Illness Middle Aged Humans Male Models, Theoretical Intensive Care Units Deep Learning Retrospective Studies
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