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Trauma Severity Indices

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A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt.

International journal of injury control and safety promotion
The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequentl...

BPBSAM: Body part-specific burn severity assessment model.

Burns : journal of the International Society for Burn Injuries
BACKGROUND AND OBJECTIVE: Burns are a serious health problem leading to several thousand deaths annually, and despite the growth of science and technology, automated burns diagnosis still remains a major challenge. Researchers have been exploring vis...

Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.

Chinese journal of traumatology = Zhonghua chuang shang za zhi
PURPOSE: The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse pr...

Relationship between Machine-Learning Image Classification of T-Weighted Intramedullary Hypointensity on 3 Tesla Magnetic Resonance Imaging and Clinical Outcome in Dogs with Severe Spinal Cord Injury.

Journal of neurotrauma
Early prognostic information in cases of severe spinal cord injury can aid treatment planning and stratification for clinical trials. Analysis of intraparenchymal signal change on magnetic resonance imaging has been suggested to inform outcome predic...

Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of old...

Clinical decision support for severe trauma patients: Machine learning based definition of a bundle of care for hemorrhagic shock and traumatic brain injury.

The journal of trauma and acute care surgery
BACKGROUND: Deviation from guidelines is frequent in emergency situations, and this may lead to increased mortality. Probably because of time constraints, 55% is the greatest reported guidelines compliance rate in severe trauma patients. This study a...

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

Journal of medical Internet research
BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the In...

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the st...

Prediction of mortality among severely injured trauma patients A comparison between TRISS and machine learning-based predictive models.

Injury
BACKGROUND: Given the huge impact of trauma on hospital systems around the world, several attempts have been made to develop predictive models for the outcomes of trauma victims. The most used, and in many studies most accurate predictive model, is t...

Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models.

Accident; analysis and prevention
Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both unc...