A computationally advantageous system for fitting probabilistic decompression models to empirical data.

Journal Article (Journal Article)

To investigate the nature and mechanisms of decompression sickness (DCS), we developed a system for evaluating the success of decompression models in predicting DCS probability from empirical data. Model parameters were estimated using maximum likelihood techniques. Exact integrals of risk functions and tissue kinetics transition times were derived. Agreement with previously published results was excellent including: (a) maximum likelihood values within one log-likelihood unit of previous results and improvements by re-optimization; (b) mean predicted DCS incidents within 1.4% of observed DCS; and (c) time of DCS occurrence prediction. Alternative optimization and homogeneous parallel processing techniques yielded faster model optimization times.

Full Text

Duke Authors

Cited Authors

  • Howle, LE; Weber, PW; Vann, RD

Published Date

  • December 2009

Published In

Volume / Issue

  • 39 / 12

Start / End Page

  • 1117 - 1129

PubMed ID

  • 19853847

Electronic International Standard Serial Number (EISSN)

  • 1879-0534

International Standard Serial Number (ISSN)

  • 0010-4825

Digital Object Identifier (DOI)

  • 10.1016/j.compbiomed.2009.09.006


  • eng