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Analytic gain in probabilistic decompression sickness models.

Publication ,  Journal Article
Howle, LE
Published in: Computers in biology and medicine
November 2013

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.

Duke Scholars

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

November 2013

Volume

43

Issue

11

Start / End Page

1739 / 1747

Related Subject Headings

  • Survival Analysis
  • Models, Statistical
  • Models, Biological
  • Humans
  • Decompression Sickness
  • Computational Biology
  • Biomedical Engineering
  • Algorithms
  • 4601 Applied computing
  • 4203 Health services and systems
 

Citation

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ICMJE
MLA
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Howle, L. E. (2013). Analytic gain in probabilistic decompression sickness models. Computers in Biology and Medicine, 43(11), 1739–1747. https://doi.org/10.1016/j.compbiomed.2013.07.026
Howle, Laurens E. “Analytic gain in probabilistic decompression sickness models.Computers in Biology and Medicine 43, no. 11 (November 2013): 1739–47. https://doi.org/10.1016/j.compbiomed.2013.07.026.
Howle LE. Analytic gain in probabilistic decompression sickness models. Computers in biology and medicine. 2013 Nov;43(11):1739–47.
Howle, Laurens E. “Analytic gain in probabilistic decompression sickness models.Computers in Biology and Medicine, vol. 43, no. 11, Nov. 2013, pp. 1739–47. Epmc, doi:10.1016/j.compbiomed.2013.07.026.
Howle LE. Analytic gain in probabilistic decompression sickness models. Computers in biology and medicine. 2013 Nov;43(11):1739–1747.
Journal cover image

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

November 2013

Volume

43

Issue

11

Start / End Page

1739 / 1747

Related Subject Headings

  • Survival Analysis
  • Models, Statistical
  • Models, Biological
  • Humans
  • Decompression Sickness
  • Computational Biology
  • Biomedical Engineering
  • Algorithms
  • 4601 Applied computing
  • 4203 Health services and systems