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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer
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Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer

机译：肺癌脑转移患者短期生存期的概率模型

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摘要

The prediction of substantially short survivability in patients is extremely risky. In this study, we proposed a probabilistic model using Bayesian network (BN) to predict the short survivability of patients with brain metastasis from lung cancer. A nationwide cancer patient database from 1996 to 2010 in Taiwan was used. The cohort consisted of 438 patients with brain metastasis from lung cancer. We utilized synthetic minority over-sampling technique (SMOTE) to solve the imbalanced property embedded in the problem. The proposed BN was compared with three competitive models, namely, naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Statistical analysis showed that performances of BN, LR, NB, and SVM were statistically the same in terms of all indices with low sensitivity when these models were applied on an imbalanced data set. Results also showed that SMOTE can improve the performance of the four models in terms of sensitivity, while keeping high accuracy and specificity. Further, the proposed BN is more effective as compared with NB, LR, and SVM from two perspectives: the transparency and ability to show the relation of factors affecting brain metastasis from lung cancer; it allows decision makers to find the probability despite incomplete evidence and information; and the sensitivity of the proposed BN is the highest among all standard machine learning methods. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：预测患者生存期很短是非常危险的。在这项研究中，我们提出了一种使用贝叶斯网络（BN）的概率模型来预测患有肺癌的脑转移患者的短期生存能力。使用了1996年至2010年台湾的全国癌症患者数据库。该队列包括438例肺癌脑转移患者。我们利用合成少数样本过采样技术（SMOTE）解决了问题中嵌入的不平衡特性。将提议的BN与三种竞争模型进行比较，即朴素贝叶斯（NB），逻辑回归（LR）和支持向量机（SVM）。统计分析表明，当将这些模型应用于不平衡数据集时，BN，LR，NB和SVM的性能在所有指标上具有相同的敏感性，但灵敏度较低。结果还表明，SMOTE可以在灵敏度方面提高四个模型的性能，同时保持较高的准确性和特异性。此外，从以下两个方面来看，拟议的BN与NB，LR和SVM相比更为有效：透明性和显示影响肺癌脑转移因素之间关系的能力;它使决策者能够找到证据和信息不完整的可能性;在所有标准的机器学习方法中，所提出的BN的敏感性最高。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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