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首页> 外文会议>IEEE International symposium on computer-based medical systems >Preliminary Study for a Bayesian Network Prognostic Model for Crohn's Disease
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Preliminary Study for a Bayesian Network Prognostic Model for Crohn's Disease

机译：贝叶斯网络克罗恩病预后模型的初步研究

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Crohn's disease is one type of inflammatory bowel disease whose incidence is currently increasing, and may affect any part of both the small and large intestine, possibly irritating deeper layers of the organs. Being a chronic disease, neither treatment nor surgery actually heals the patients. Thus, focus has been given to identifying good prognostic models based on clinical factors since they are more easily included in daily practice. The aim of this work is to provide an initial study on the adequacy of a Bayesian network model to enhance the prognosis prediction for patients with Crohn's disease. Multicentric study data of patients with surgery or immuno suppression in the six month after diagnosis was used to derive a Bayesian network, focusing on the prognosis and the analysis of factors interaction, including clinical features, disease course, treatment, follow-up plan, and adverse events. Two models were evaluated (naïve Bayes and Tree-Augmented Naïve Bayes) and also compared with logistic regression, using cross-validation and ROC curve analysis. Preliminary results showed competitive accuracy (above 75%) and discriminative power (above 70%). The generated models presented interesting insights on factor interaction and predictive ability for the prognosis, supporting their use in future clinical decision support systems.
机译：克罗恩氏病是一种炎症性肠病，目前其发病率正在增加，并且可能影响小肠和大肠的任何部分，可能会刺激更深的器官层。作为一种慢性疾病，无论是治疗还是手术都无法真正治愈患者。因此，由于基于临床因素的良好预后模型更易于在日常实践中使用，因此已将重点放在确定这些模型上。这项工作的目的是对贝叶斯网络模型的适用性进行初步研究，以增强克罗恩病患者的预后预测。诊断后六个月中接受手术或免疫抑制治疗的患者的多中心研究数据被用于得出贝叶斯网络，重点是对因素相互作用的预后和分析，包括临床特征，疾病进程，治疗，随访计划和不良事件。评估了两个模型（朴素贝叶斯和树增强朴素贝叶斯），并使用交叉验证和ROC曲线分析与逻辑回归进行了比较。初步结果显示出竞争准确性（高于75％）和判别力（高于70％）。生成的模型对因子相互作用和预后的预测能力提出了有趣的见解，支持它们在未来的临床决策支持系统中使用。

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