Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.
Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation.
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使用条件依赖性和多重插补对癫痫进行多模式诊断。
DOI：
10.1109/prni.2014.6858526
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
E
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作者：
Kerr,WesleyT;Hwang,EricS;Raman,KaavyaR;Barritt,SarahE;Patel,AkashB;Le,JustineM;Hori,JessicaM;Davis,EmilyC;Braesch,ChelseaT;Janio,EmilyA;Lau,EdwardP;Cho,AndrewY;Anderson,Ariana;Silverman,DanielHS;Salamon,Noriko;E
如果存在癫痫类型的话，对耐药性癫痫发作障碍的明确诊断是基于临床信息、长期视频脑电图（EEG）和神经影像学的有效结合。诊断是由一个共识小组，结合这些不同的方式使用临床智慧和经验。在这里，我们比较了两种方法的多模式计算机辅助诊断，矢量连接（VC）和条件依赖（CD），使用临床档案数据从645例耐药癫痫发作障碍，证实了视频脑电图。CD模拟临床决策过程，而VC允许跨模态交互的统计建模。由于临床数据的性质，并非所有患者的所有信息均可用。为了克服这一点，我们对缺失数据进行了多重插补。使用C4.5决策树，单模态分类器在区分非癫痫性发作、颞叶癫痫、其他局灶性癫痫和全身性癫痫时，MRI、临床信息和FDG-PET的平均准确率分别为53.1%、51.5%和51.1%（与机会相比，p<0.01）。使用VC，平均准确率显著较低（39.2%）。相比之下，用MRI然后用临床信息分类的CD分类器实现了58.7%的平均准确度（vs. VC，p<0.01）。与MRI分类器相比，VC的准确性降低说明了添加更多信息特征不会单调地提高性能。条件依赖优于向量连接，这表明条件依赖施加的结构提高了我们对多模态数据中的潜在诊断趋势进行建模的能力。
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between nonepileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p<;0.01). Using VC, the average accuracy was significantly lower (39.2 %). In contrast, the CD classifier that classified with MRI then clinical information achieved an average accuracy of 58.7% (vs. VC, p<;0.01). The decrease in accuracy of VC compared to the MRI classifier illustrates how the addition of more informative features does not improve performance monotonically. The superiority of conditional dependence over vector concatenation suggests that the structure imposed by conditional dependence improved our ability to model the underlying diagnostic trends in the multimodality data.