随着电子健康记录(Electronic Health Record,EHR)的出现与广泛应用,基于EHR数据的预测模型可以起到早期检测和干预疾病的作用。异质属性在EHR数据中普遍存在,但是难以做到深度利用,因此可通过对数据样本进行异质属性融合的方法,为后续...随着电子健康记录(Electronic Health Record,EHR)的出现与广泛应用,基于EHR数据的预测模型可以起到早期检测和干预疾病的作用。异质属性在EHR数据中普遍存在,但是难以做到深度利用,因此可通过对数据样本进行异质属性融合的方法,为后续模型训练提供信息丰富的数据表征基础。本文设计一种高效的二阶段预测模型,用于解决重疾预测中存在的时效与成本等问题。该模型的第一阶段对病例样本进行粗粒度预测,将危重程度低的病例进行疾病初筛,起到提前分流病人的作用;第二阶段模型则基于第一阶段的粗滤结果,对潜在的危重病例进行更细粒度的预测。通过实验验证,经过异质属性融合处理后,在选择前6个时间点构造非时序模型时,二阶段模型可以较好地兼具疾病初筛以及疾病预测的效果。展开更多
3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However,...3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset- based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.展开更多
文摘随着电子健康记录(Electronic Health Record,EHR)的出现与广泛应用,基于EHR数据的预测模型可以起到早期检测和干预疾病的作用。异质属性在EHR数据中普遍存在,但是难以做到深度利用,因此可通过对数据样本进行异质属性融合的方法,为后续模型训练提供信息丰富的数据表征基础。本文设计一种高效的二阶段预测模型,用于解决重疾预测中存在的时效与成本等问题。该模型的第一阶段对病例样本进行粗粒度预测,将危重程度低的病例进行疾病初筛,起到提前分流病人的作用;第二阶段模型则基于第一阶段的粗滤结果,对潜在的危重病例进行更细粒度的预测。通过实验验证,经过异质属性融合处理后,在选择前6个时间点构造非时序模型时,二阶段模型可以较好地兼具疾病初筛以及疾病预测的效果。
基金supported by the National Natural Science Foundation of China(No.41604107)the Scientific Research Staring Foundation of University of Electronic Science and Technology of China(No.ZYGX2015KYQD049)
文摘3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset- based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.
