AIMC Topic: Sepsis

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Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

Shock (Augusta, Ga.) Jul 1, 2021
BACKGROUND: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a ...
Humans Artificial Intelligence Prognosis Male Middle Aged Sepsis Time Factors Retrospective Studies Critical Illness Female Aged
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Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Medicine May 14, 2021
Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning m...
Aged, 80 and over Young Adult Clinical Decision Rules Adult Male ROC Curve Middle Aged Logistic Models Sepsis Support Vector Machine Adolescent Decision Trees Hospital Mortality Algorithms Intensive Care Units Female Aged Humans Machine Learning
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Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.

Journal of the American Medical Informatics Association : JAMIA Mar 18, 2021
OBJECTIVE: Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs)...
Prognosis Female Male Infant Humans Machine Learning Anti-Infective Agents ROC Curve Treatment Outcome Area Under Curve Central Venous Catheters Sepsis Adolescent Child, Preschool Child Cross Infection Recurrence
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Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.

Critical care medicine Dec 1, 2020
OBJECTIVES: Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and machine learning techniques for early prediction of sepsis.
Sepsis Early Diagnosis Early Warning Score Intensive Care Units Female Male Humans Machine Learning Algorithms
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Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

Critical care medicine Dec 1, 2020
OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for se...
Early Diagnosis Neural Networks, Computer Artificial Intelligence Intensive Care Units Electronic Health Records Reproducibility of Results Deep Learning Sepsis Supervised Machine Learning Humans
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An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

Critical care medicine Nov 1, 2020
OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial inte...
Humans Male Early Diagnosis Electronic Health Records Artificial Intelligence Middle Aged Female Sepsis Risk Factors Algorithms Models, Statistical Aged Retrospective Studies Intensive Care Units
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A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

Critical care medicine Oct 1, 2020
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...
Time Factors Critical Care Intensive Care Units Humans Reproducibility of Results Early Diagnosis Machine Learning Severity of Illness Index Retrospective Studies Sepsis Vital Signs
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A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

The journal of trauma and acute care surgery Oct 1, 2020
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...
Sodium Critical Care Proportional Hazards Models Multivariate Analysis Injury Severity Score Organ Dysfunction Scores Wounds and Injuries Male Middle Aged Retrospective Studies Sepsis Hospitalization Humans Critical Illness ROC Curve Young Adult Neural Networks, Computer Intensive Care Units Adult Machine Learning Female Prognosis
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Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategie...
Early Diagnosis Sepsis Humans Child Neural Networks, Computer Intensive Care Units, Pediatric Prognosis Heart Rate
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Practical Machine Learning-Based Sepsis Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the nonspecific clinical signs and the absence of gold standard diagnostics. However, early recognition o...
Humans Machine Learning Sepsis Critical Care
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