Analytic gain in probabilistic decompression sickness models.

Journal Article (Journal Article)

Decompression sickness (DCS) is a disease known to be related to inert gas bubble formation originating from gases dissolved in body tissues. Probabilistic DCS models, which employ survival and hazard functions, are optimized by fitting model parameters to experimental dive data. In the work reported here, I develop methods to find the survival function gain parameter analytically, thus removing it from the fitting process. I show that the number of iterations required for model optimization is significantly reduced. The analytic gain method substantially improves the condition number of the Hessian matrix which reduces the model confidence intervals by more than an order of magnitude.

Full Text

Duke Authors

Cited Authors

  • Howle, LE

Published Date

  • November 2013

Published In

Volume / Issue

  • 43 / 11

Start / End Page

  • 1739 - 1747

PubMed ID

  • 24209920

Electronic International Standard Serial Number (EISSN)

  • 1879-0534

International Standard Serial Number (ISSN)

  • 0010-4825

Digital Object Identifier (DOI)

  • 10.1016/j.compbiomed.2013.07.026


  • eng