AIMC Topic: Trauma Centers

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A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports.

Emergency radiology Jan 28, 2019
PURPOSE: Subdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage, and radiographic characteristics of SDH are predictive of complications and patient outcomes. We created a natural language processing (NLP) algorithm to e...
Trauma Centers Hematoma, Subdural Humans Tomography, X-Ray Computed Reproducibility of Results Natural Language Processing
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Communication with Orthopedic Trauma Patients via an Automated Mobile Phone Messaging Robot.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association Dec 20, 2017
BACKGROUND: Communication with orthopedic trauma patients is traditionally problematic with low response rates (RRs). The purpose of this investigation was to (1) evaluate the feasibility of communicating with orthopedic trauma patients postoperative...
Text Messaging Orthopedics Trauma Centers Musculoskeletal System Male Humans United States Pain, Postoperative Prospective Studies Robotics Feasibility Studies Female Pain Measurement Analgesics, Opioid Adult Pain Management Wounds and Injuries
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Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.

The American journal of emergency medicine Nov 3, 2016
OBJECTIVES: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patientsā‰„age 65years who have incurred minor head i...
Neural Networks, Computer Forecasting Retrospective Studies Tomography, X-Ray Computed Female Aged Male Accidental Falls Decision Support Systems, Clinical Humans United States Craniocerebral Trauma Regression Analysis Cost-Benefit Analysis Trauma Centers
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Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine Feb 13, 2016
OBJECTIVES: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns wit...
Logistic Models Emergency Service, Hospital Trauma Centers Humans Sepsis Machine Learning Hospital Mortality ROC Curve Decision Support Systems, Clinical Aged Retrospective Studies Aged, 80 and over Electronic Health Records Adult Female Male Hospitalization Middle Aged
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Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study.

PloS one Aug 21, 2015
Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of...
Injury Severity Score Multiple Trauma Humans Blood Component Transfusion Outcome Assessment, Health Care Adult Machine Learning Trauma Centers Survival Analysis Male Prospective Studies Hemorrhage Middle Aged Female Hospital Mortality
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Variable importance and prediction methods for longitudinal problems with missing variables.

PloS one Mar 27, 2015
We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patie...
Evidence-Based Medicine Wounds and Injuries Trauma Centers Algorithms Prognosis Longitudinal Studies Predictive Value of Tests Humans Machine Learning
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Are rib fractures stable? An analysis of progressive rib fracture offset in the acute trauma setting.

The journal of trauma and acute care surgery Dec 1, 2021
BACKGROUND: Rib fractures serve as both a marker of injury severity and a guide for clinical decision making for trauma patients. Although recent studies have suggested that rib fractures are dynamic, the degree of progressive offset remains unknown....
Retrospective Studies Tomography, X-Ray Computed Clinical Decision-Making Female Male Humans Outcome Assessment, Health Care Neural Networks, Computer Middle Aged Quality Improvement Rib Fractures Disease Progression Injury Severity Score Thoracic Injuries Patient Care Trauma Centers Lung Diseases Flail Chest
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Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

The journal of trauma and acute care surgery Jun 1, 2021
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome ...
Humans Artificial Intelligence Thoracic Injuries Emergency Medical Services Triage Male Feasibility Studies Risk Assessment Hemorrhage Retrospective Studies ROC Curve Shock Female Blood Transfusion Injury Severity Score Young Adult Models, Cardiovascular Adult Trauma Centers Wounds, Gunshot
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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...
Male Hemorrhage Erythrocytes Blood Platelets Plasma Humans Machine Learning Female Trauma Centers Intention to Treat Analysis Blood Component Transfusion Wounds and Injuries Hemostasis North America
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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 ...
Geography, Medical Humans Neural Networks, Computer United States Bayes Theorem Injury Severity Score Wounds and Injuries Trauma Centers
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