The Use of Polynomial Neural Networks for Mortality Prediction in Uncontrolled Venous and Arterial Hemorrhage

Abstract

The ability to rapidly and accurately triage, evacuate, and utilize appropriate interventions can be problematic in the early decision-making process of trauma care. With current methods of pre-hospital data collection and analysis, decisions are often based upon single data points. This information may be insufficient for reliable decision-making. To date, no studies have attempted to utilize data at multiple time points for purposes of enhancing prediction, nor have studies attempted to synthesize prediction models with data reflecting both large-vessel venous and arterial injuries. Therefore, we performed a retrospective study to examine the potential utility of dynamic neural networks in predicting mortality using highly discretized uncontrolled hemorrhagic shock data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA635655

Entities

People

  • Anthony E. Pusateri
  • B. E. Parker Jr.
  • David A. Roberts
  • David E. Sweenor
  • Jeffrey S. Young
  • Jill L. Sondeen
  • John B Holcomb
  • William J. Brady Jr.

Organizations

  • United States Army Institute of Surgical Research

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Cardiovascular Physiological Phenomena
  • Data Analysis
  • Data Science
  • Databases
  • Digital Data
  • Health Services
  • Hemorrhage
  • Hemorrhagic Shock
  • Information Science
  • Measuring Instruments
  • Medical Personnel
  • Neural Networks
  • Patient Care
  • Polynomials
  • Standards
  • Statistical Analysis

Fields of Study

  • Medicine

Readers

  • Computational Modeling and Simulation
  • Trauma Surgery or Emergency Medicine.
  • Trauma or Military Medicine

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks