Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS.
翻译:临床结果预测在中风病人管理中起着重要作用。 从机器学习角度看,主要挑战之一是病人入院时的多种数据,即多维图像数据,以及卡路里临床数据。在本文中,提出了基于长期短期记忆(CNN-LSTM)的多式进化神经网络模型。对于每个MR图像模块,一个专门的网络利用修改的Ranin尺度,对临床结果进行初步预测。通过将每个模块的初步概率与每个模块用于特定类型MR图像的初步概率结合起来,根据临床元数据加权,即本地年龄或国家卫生系统规模(NIHSSS),获得最后的MS分数。实验结果表明,拟议的模型超过了基线,为在深层学习结构中自动编码MSM图像的波段-时空环境提供了一种原始方法。与NIHSS相比,拟议模型实现了最高AUC(0.77)。