A computationally advantageous system for fitting probabilistic decompression models to empirical data.
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.
Howle
Laurens E
LE
Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708-0300, USA. laurens.howle@duke.edu
Weber
Paul W
PW
Vann
Richard D
RD
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003665:Decompression Sickness / Q000209:etiology* / Q000503:physiopathology; D004242:Diving / Q000009:adverse effects / Q000502:physiology; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008954:Models, Biological; D015233:Models, Statistical; D011312:Pressure; D013997:Time Factors