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首页> 外文会议>IEEE International symposium on computer-based medical systems >Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited
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Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited

机译：阻塞性睡眠呼吸暂停诊断：再谈贝​​叶斯网络模型。

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Obstructive Sleep Apnea (OSA) is a disease that affects approximately 4% of men and 2% of women worldwide but is still underestimated and underdiagnosed. The standard method for assessing this index, and therefore defining the OSA diagnosis, is polysomnography (PSG). Previous work developed relevant Bayesian network models but those were based only on variables univariatedly associated with the outcome, yielding a bias on the possible knowledge representation of the models. The aim of this work was to develop and validate new Bayesian network decision support models that could be used during sleep consult to assess the need for PSG. Bayesian models were developed using a) expert opinion, b) hill-climbing, c) naïve Bayes and d) TAN structures. Resulting models validity was assessed with in-sample AUC and stratified cross-validation, also comparing with previously published model. Overall, models achieved good discriminative power (AUC>70%) and validity (measures consistently above 70%). Main conclusions are a) the need to integrate a wider range of variables in the final models and b) the support of using Bayesian networks in the diagnosis of obstructive sleep apnea.
机译：阻塞性睡眠呼吸暂停（OSA）是一种疾病，影响全世界约4％的男性和2％的女性，但仍被低估和诊断不足。评估该指标，从而定义OSA诊断的标准方法是多导睡眠图（PSG）。先前的工作开发了相关的贝叶斯网络模型，但这些模型仅基于与结果单变量相关的变量，从而对模型的可能知识表示产生了偏见。这项工作的目的是开发和验证新的贝叶斯网络决策支持模型，该模型可以在睡眠咨询期间使用，以评估对PSG的需求。使用以下方法开发贝叶斯模型：a）专家意见，b）爬山，c）朴素的贝叶斯和d）TAN结构。通过样本内AUC和分层交叉验证对所得模型的有效性进行了评估，并与先前发布的模型进行了比较。总体而言，模型获得了良好的判别力（AUC> 70％）和有效性（测量值始终高于70％）。主要结论是：a）需要在最终模型中整合更广泛的变量，以及b）支持使用贝叶斯网络来诊断阻塞性睡眠呼吸暂停。

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