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Design and Analysis for Fall Detection System Simplification
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Predicting factors for extremity fracture among border-fall patients using machine learning computing.

Carlos Palacio1, Maximillian Hovorka1, Marie Acosta1

  • 1South Texas Health System - McAllen Department of Trauma, McAllen, TX, 78503, USA.

Heliyon
|July 4, 2024
PubMed
Summary
This summary is machine-generated.

Injuries at the US-Mexican border are linked to specific fall mechanisms. Understanding these factors, like falls from border walls or vehicle ejections, is crucial for predicting and treating anatomical injuries.

Keywords:
Border fallExtremity fractureMPNNMachine learning

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Area of Science:

  • Trauma Surgery
  • Public Health
  • Biomechanics

Background:

  • Falls at the US-Mexican border result from diverse factors including border wall/fence falls, fleeing border patrols, and vehicle ejections.
  • These incidents lead to significant anatomical injuries, including limb fractures and internal organ damage.
  • Identifying the specific causes and their associated injury patterns is critical for clinical management.

Purpose of the Study:

  • To determine the factors contributing to anatomical injuries sustained at the US-Mexican border.
  • To identify and rank the importance of factors leading to limb fractures and internal organ injuries.
  • To develop a predictive model for clinical applications based on multifactorial assessment.

Main Methods:

  • A retrospective study of 178 patients admitted with musculoskeletal or internal organ injuries.
  • Analysis of demographic, comorbidity, and falling mechanic factors.
  • Utilized Multilayer Perceptron Neural Network (MPNN) for predictive factor identification and importance stratification.

Main Results:

  • Extremity fractures correlated significantly with border wall/fence falls (p=0.001) and fleeing (p=0.002).
  • Spine fractures were associated with bridge/fence jumps (p=0.007, p=0.026).
  • Vehicle ejections/MVAs strongly correlated with head, chest, and abdominal injuries (P<0.001).

Conclusions:

  • Different injury factors lead to distinct anatomical injuries.
  • Multifactorial assessment enhances diagnostic accuracy for border-related injuries.
  • The findings support the development of predictive models for clinical use.