TY - JOUR
T1 - 3D-CNN-SPP
T2 - A patient risk prediction system from electronic health records via 3D CNN and spatial pyramid pooling
AU - Ju, Ronghui
AU - Zhou, Pan
AU - Wen, Shiping
AU - Wei, Wei
AU - Xue, Yuan
AU - Huang, Xiaolei
AU - Yang, Xin
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grants 61972448, 61602197, and 61872417.
Publisher Copyright:
© 2017 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.
AB - The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.
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U2 - 10.1109/TETCI.2019.2960474
DO - 10.1109/TETCI.2019.2960474
M3 - Article
AN - SCOPUS:85089760345
SN - 2471-285X
VL - 5
SP - 247
EP - 261
JO - IEEE Transactions on Emerging Topics in Computational Intelligence
JF - IEEE Transactions on Emerging Topics in Computational Intelligence
IS - 2
M1 - 8949441
ER -