AIMC Topic: Wounds, Penetrating

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Development of a Machine Learning-Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma.

JAMA surgery Oct 1, 2023
IMPORTANCE: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services.
Humans Machine Learning Female Adult Male Middle Aged United States Health Services Accessibility Healthcare Disparities Patient Discharge Black or African American White Wounds, Penetrating
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Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

The journal of trauma and acute care surgery Jul 1, 2021
BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linea...
Humans Artificial Intelligence Decision Support Techniques Databases, Factual Wounds, Nonpenetrating Female Risk Factors Aged Adult Male Middle Aged Risk Assessment Emergencies Hospital Mortality Smartphone United States Predictive Value of Tests Injury Severity Score Wounds, Penetrating
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Machine Learning Models for Prediction of Reinjury After Penetrating Trauma.

JAMA surgery Feb 1, 2018
This study compares several machine learning models for prediction of reinjury after penetrating trauma.
Humans Machine Learning Forecasting Mental Disorders Databases, Factual Suicide United States Models, Theoretical Patient Readmission Self-Injurious Behavior Recurrence Wounds, Penetrating
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