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Trinomial decompression sickness model using full, marginal, and non-event outcomes.

Publication ,  Journal Article
King, AE; Andriano, NR; Howle, LE
Published in: Computers in biology and medicine
March 2020

Decompression sickness (DCS) is a condition associated with reductions in ambient pressure during underwater diving and altitude exposure. Determining the risk of DCS from a dive exposure remains an active area of research, with the goal of developing safe decompression schedules to mitigate the occurrence of DCS. This work develops a probabilistic model for the trinomial outcome of full, marginal, and no DCS. The model treats full DCS and marginal DCS as separate, fully weighted hierarchical events. Six variants of exponential-exponential (EE) and linear-exponential (LE) decompression models were optimized to fit dive outcomes from the BIG292 empirical human dive trial data of 3322 exposures. Using the log likelihood difference test, the LE1 trinomial marginal model was determined to provide the best fit for the data. The LE1 trinomial marginal model can be used to better understand decompression schedules, expanding upon binomial models which treat marginal DCS as either a fractionally weighted event or a non-event. Future work could investigate whether the use of marginal DCS cases improves multinomial probabilistic DCS model performance. Model improvement could include the addition of a fourth outcome, where full DCS is split and categorized as serious or mild DCS, creating a tetranomial model with serious, mild, marginal, and no DCS outcomes for comparison with the presently developed model.

Duke Scholars

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

March 2020

Volume

118

Start / End Page

103640

Related Subject Headings

  • Probability
  • Models, Statistical
  • Humans
  • Diving
  • Decompression Sickness
  • Biomedical Engineering
  • 4601 Applied computing
  • 4203 Health services and systems
  • 3102 Bioinformatics and computational biology
  • 11 Medical and Health Sciences
 

Citation

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King, A. E., Andriano, N. R., & Howle, L. E. (2020). Trinomial decompression sickness model using full, marginal, and non-event outcomes. Computers in Biology and Medicine, 118, 103640. https://doi.org/10.1016/j.compbiomed.2020.103640
King, Amy E., Nicholas R. Andriano, and Laurens E. Howle. “Trinomial decompression sickness model using full, marginal, and non-event outcomes.Computers in Biology and Medicine 118 (March 2020): 103640. https://doi.org/10.1016/j.compbiomed.2020.103640.
King AE, Andriano NR, Howle LE. Trinomial decompression sickness model using full, marginal, and non-event outcomes. Computers in biology and medicine. 2020 Mar;118:103640.
King, Amy E., et al. “Trinomial decompression sickness model using full, marginal, and non-event outcomes.Computers in Biology and Medicine, vol. 118, Mar. 2020, p. 103640. Epmc, doi:10.1016/j.compbiomed.2020.103640.
King AE, Andriano NR, Howle LE. Trinomial decompression sickness model using full, marginal, and non-event outcomes. Computers in biology and medicine. 2020 Mar;118:103640.
Journal cover image

Published In

Computers in biology and medicine

DOI

EISSN

1879-0534

ISSN

0010-4825

Publication Date

March 2020

Volume

118

Start / End Page

103640

Related Subject Headings

  • Probability
  • Models, Statistical
  • Humans
  • Diving
  • Decompression Sickness
  • Biomedical Engineering
  • 4601 Applied computing
  • 4203 Health services and systems
  • 3102 Bioinformatics and computational biology
  • 11 Medical and Health Sciences