Clinical outcome prediction in aneurysmal subarachnoid hemorrhage using Bayesian neural networks with fuzzy logic inferences.
Clinical outcome prediction in aneurysmal subarachnoid hemorrhage using Bayesian neural networks with fuzzy logic inferences.
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DOI：
10.1155/2013/904860
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发表时间：
2013
影响因子：
--
通讯作者：
Levine MA
中科院分区：
文献类型：
--
作者：
Lo BW;Macdonald RL;Baker A;Levine MA
客观的。创建了具有模糊逻辑推理的贝叶斯神经网络的新型临床预测方法，并将其应用于推导脑动脉瘤蛛网膜下腔出血（aSAH）的预后决策规则。方法。将具有模糊逻辑推理的贝叶斯神经网络方法应用于 Tirilazad 治疗动脉瘤性蛛网膜下腔出血的五项试验（3551 名患者）的数据。结果。对 aSAH 预后因素观察性研究的贝叶斯荟萃分析给出了群体平均对数奇数比 (OR) 的可推广后验分布。贝叶斯和线性回归 OR 中也出现了类似的趋势。重要的结果预测因素包括正常运动反应、脑梗塞、心肌梗塞病史、脑水肿、糖尿病病史、第8天发烧、既往蛛网膜下腔出血、入院血管造影血管痉挛、神经系统分级、脑室内出血、破裂动脉瘤大小、高血压病史、血管痉挛天数、年龄和平均动脉压。未转换的数据集中存在异方差性。人工神经网络使用多层感知器模型发现 1 层中 11 个隐藏变量的非线性关系。模糊逻辑决策规则（质心去模糊化技术）表示大于 2.5 个簇的不良预后的分界点。讨论。该 aSAH 预后系统利用现有知识，识别未知区域，结合临床推理，并补偿预测中的不确定性。
Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
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影响因子：
4.1
作者：
HALEY, EC;KASSELL, NF;HANSEN, CA
通讯作者：
HANSEN, CA
影响因子：
3.5
作者：
Temes, Richard E.;Tessitore, Elena;Parra, Augusto
通讯作者：
Parra, Augusto
影响因子：
3.5
作者：
Jaja, Blessing N. R.;Cusimano, Michael D.;Macdonald, R. Loch
通讯作者：
Macdonald, R. Loch
影响因子：
4.1
作者：
Haley, EC;Kassell, NF;Alves, WM
通讯作者：
Alves, WM
影响因子：
8.8
作者：
Kahn, JM;Caldwell, EC;Rubenfeld, GD
通讯作者：
Rubenfeld, GD