Weathersby P K, Survanshi S S, Homer L D, Parker E, Thalmann E D
Naval Submarine Medical Research Laboratory, Groton, Connecticut 06349-5900.
J Appl Physiol (1985). 1992 Apr;72(4):1541-8. doi: 10.1152/jappl.1992.72.4.1541.
Probabilistic models and maximum likelihood estimation have been used to predict the occurrence of decompression sickness (DCS). We indicate a means of extending the maximum likelihood parameter estimation procedure to make use of knowledge of the time at which DCS occurs. Two models were compared in fitting a data set of nearly 1,000 exposures, in which greater than 50 cases of DCS have known times of symptom onset. The additional information provided by the time at which DCS occurred gave us better estimates of model parameters. It was also possible to discriminate between good models, which predict both the occurrence of DCS and the time at which symptoms occur, and poorer models, which may predict only the overall occurrence. The refined models may be useful in new applications for customizing decompression strategies during complex dives involving various times at several different depths. Conditional probabilities of DCS for such dives may be reckoned as the dive is taking place and the decompression strategy adjusted to circumstance. Some of the mechanistic implications and the assumptions needed for safe application of decompression strategies on the basis of conditional probabilities are discussed.
概率模型和最大似然估计已被用于预测减压病（DCS）的发生。我们指出了一种扩展最大似然参数估计程序的方法，以利用DCS发生时间的知识。在拟合一个近1000次暴露的数据集时，对两个模型进行了比较，其中50多例DCS病例有已知的症状发作时间。DCS发生时间提供的额外信息使我们能够更好地估计模型参数。区分能够预测DCS发生以及症状出现时间的好模型和可能仅预测总体发生情况的较差模型也是可能的。改进后的模型在涉及不同深度不同时间的复杂潜水过程中定制减压策略的新应用中可能会很有用。对于此类潜水，DCS的条件概率可以在潜水进行时计算出来，并根据情况调整减压策略。讨论了基于条件概率安全应用减压策略所需的一些机制含义和假设。
