AIMC Topic: Wounds and Injuries

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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...
Male Middle Aged Sepsis ROC Curve Hospitalization Young Adult Adult Multivariate Analysis Injury Severity Score Organ Dysfunction Scores Wounds and Injuries Sodium Critical Care Proportional Hazards Models Neural Networks, Computer Machine Learning Prognosis Intensive Care Units Female Retrospective Studies Critical Illness Humans
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Dynamic impact of transfusion ratios on outcomes in severely injured patients: Targeted machine learning analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios randomized clinical trial.

The journal of trauma and acute care surgery Sep 1, 2020
BACKGROUND: Massive transfusion protocols to treat postinjury hemorrhage are based on predefined blood product transfusion ratios followed by goal-directed transfusion based on patient's clinical evolution. However, it remains unclear how these trans...
Humans Machine Learning Erythrocytes Blood Platelets Plasma Blood Component Transfusion Female Male Hemorrhage Trauma Centers Intention to Treat Analysis Wounds and Injuries Hemostasis North America
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Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography.

Korean journal of radiology Jul 1, 2020
OBJECTIVE: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT.
Wounds and Injuries Whole Body Imaging Middle Aged Deep Learning Image Interpretation, Computer-Assisted Rib Fractures Retrospective Studies Tomography, X-Ray Computed Adult Male Female Aged Humans
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Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

The journal of trauma and acute care surgery May 1, 2020
BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize...
Quality Assurance, Health Care Wounds and Injuries Emergency Medical Services Natural Language Processing Quality Improvement Electronic Health Records Pilot Projects Humans
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Intelligence in injury prevention: artificial and otherwise.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention Feb 1, 2020
Artificial Intelligence Wounds and Injuries Satellite Imagery Accident Prevention Humans
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A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis Jan 1, 2020
Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30% of severely injured patients who are admitted to emergency departments (EDs). Given that conventio...
Machine Learning Algorithms Female Male Middle Aged Hospitalization Adult Humans International Normalized Ratio Blood Coagulation Disorders Wounds and Injuries Risk Assessment Emergencies Predictive Value of Tests
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Data-driven specialisation of wound care through artificial intelligence.

International wound journal Aug 1, 2019
Wounds and Injuries Decision Making, Computer-Assisted Artificial Intelligence Delivery of Health Care Inventions Humans
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Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.

The journal of trauma and acute care surgery Jul 1, 2019
BACKGROUND: Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity, and operative needs at a single trauma center with very high reliability. This model has ...
Humans Neural Networks, Computer Wounds and Injuries Trauma Centers Geography, Medical United States Bayes Theorem Injury Severity Score
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Artificial intelligence and machine learning in wound care-The wounded machine!

International wound journal Apr 1, 2019
Wounds and Injuries Humans Artificial Intelligence Machine Learning
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Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2019
OBJECTIVE: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised ma...
Middle Aged Cohort Studies ROC Curve Wounds and Injuries Trauma Centers Natural Language Processing Alcoholism Female Adult Male Humans Machine Learning Electronic Health Records
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