Trauma Care Classification. Phase 1
Abstract
This Phase I project developed a trauma care classification method based on variables that can be easily ascertained in the field environment. The major achievements of the Phase I study include: (1) Establishment of a Gaussian Potential Function Network (GPFN) architecture that allows the discrimination between various classes representing the degree of severity of the trauma classification problem. These classes constitute the basis for field triage. The GPFN is configured as an aggregate of Gaussian Potential Function Units (GPFUs); (2) Demonstration of the convergence properties of the training algorithm for the GPFN which adjusts the amplitudes, the means and the covariance matrices of the GPFUs to effect characterization of a given class as an integer value declaration; (3) Utilization of the fuzzy c-means clustering algorithm to partition the data into compact sets over which the GPFUs can be assigned. A cluster membership validity measure is also used to provide the fuzzy c-means algorithm with an estimate of the number of clusters present in the data; (4) A direct encoding classification method is also presented that allows the direct encoding of the prevalence of a given feature vector among the various classes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1996
- Accession Number
- ADB218709
Entities
People
- Ching-fang Lin