TY - A2的粉丝,Yaxiang AU -婷婷,赵盟——Kailun陈盟——一轻,陆PY - 2022 DA - 2022/09/29 TI -基于机器学习的预测研究脑出血患者血肿扩大SP - 4470134六世- 2022 AB -血肿扩大是穷人神经预后密切相关的脑出血患者(脑内出血,我)。因此,它具有重要的临床意义准确预测是否我的病人。在这项研究中,我们探索了两种模型的预测能力机器学习,机器学习(ML)方法来预测血肿扩张。该方法未特征节能CT(41计算机断层扫描,NCCT)我的病人。图像信息结合多个临床数据的预测血肿扩大。我们回顾性收集了140我的病人(包括血肿扩大患者58)从我们的医院,2021年和5616 NCCT血肿获得图像(包括2635血肿扩大的图片)和10个每个病人的临床资料。问题的对偶模型毫升方法用于这项研究包含两个步骤。第一步是使用单模预测基于深卷积神经网络(DCNN),而只使用病人。基线NCCT图像进行血肿扩大的预测。选择一个适当的DCNN模型,我们同时比较了三种DCNN模型的预测性能,包括ResNet34(34层的残余神经网络),VGGNet(视觉几何组网络),和GoogLeNet(谷歌初始网络)。 In this step, we also explored whether the method of hematoma segmentation could improve the prediction outcome. The second step is to use the dual-model predictor based on multilayer perception (MLP), where the results of the single-model predictor in step 1 are combined with multiple clinical data of the patient to predict the final result. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each model, and were predicted using the subject operating characteristic curve (the receiver operating characteristic, ROC) and area under curve (AUC) to evaluate prediction performance. The experimental results show that the ML method proposed in this study can comprehensively analyze the patient NCCT image information and clinical data, which can achieve 86.5% accuracy and have relatively equal sensitivity and specificity. Therefore, this ML method can be used as a predictive tool to effectively identify people at high risk of hematoma expansion. This study can make an effective prediction of hematoma expansion in patients with clinical cerebral hemorrhage, which can better treat patients, improve the doctor-patient relationship, reduce complications, etc. SN - 1687-725X UR - https://doi.org/10.1155/2022/4470134 DO - 10.1155/2022/4470134 JF - Journal of Sensors PB - Hindawi KW - ER -