Deep graph convolutional network for US birth data harmonization

Lishan Yu School of Biomedical Informatics, UTHealth, Houston, TX, USA; Yau Mathematical Sciences Center, Tsinghua University, Beijing, China; Beijing Institute Mathematical Sciences and Applications, Beijing, China; The majority of this work was conducted when Lishan Yu conducted her internship at UTHealth Hamisu M. Salihu Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX, USA; Center of Excellence in Health Equity, Training, and Research, Baylor College of Medicine, Houston, TX, USA Deepa Dongarwar Center of Excellence in Health Equity, Training, and Research, Baylor College of Medicine, Houston, TX, USA Luyao Chen School of Biomedical Informatics, UTHealth, Houston, TX, USA Xiaoqian Jiang School of Biomedical Informatics, UTHealth, Houston, TX, USA

TBD mathscidoc:2207.43001

Journal of Biomedical Informatics, 125, (103974), 2022.1
In this paper, we developed a feasible and efficient deep-learning-based framework to combine the United States (US) natality data for the last five decades, with changing variables and factors, into a consistent database. We constructed a graph based on the property and elements of databases, including variables, and conducted a graph convolutional network (GCN) to learn the embeddings of variables on the constructed graph, where the learned embeddings implied the similarity of variables. Specifically, we devised a loss function with a slack margin and a banlist mechanism (for a random walk) to learn the desired structure (two nodes sharing more information were more similar to each other.), and developed an active learning mechanism to conduct the harmonization. Toward a total of 9,321 variables from 49 databases (i.e., 783 stemmed variables, from 1970 to 2018), we applied our model iteratively together with human reviews for four rounds, then obtained 323 hyperchains of variables. During the harmonization, the first round of our model achieved recall and precision of 87.56%, 57.70%, respectively. Our harmonized graph neural network (HGNN) method provides a feasible and efficient way to connect relevant databases at a meta-level. Adapting to the database's property and characteristics, HGNN can learn patterns globally, which is powerful to discover the similarity between variables among databases. Our proposed method provides an effective way to reduce the manual effort in database harmonization and integration of fragmented data into useful databases for future research.
No keywords uploaded!
[ Download ] [ 2022-07-02 15:08:40 uploaded by yuls ] [ 16 downloads ] [ 0 comments ]
@inproceedings{lishan2022deep,
  title={Deep graph convolutional network for US birth data harmonization},
  author={Lishan Yu, Hamisu M. Salihu, Deepa Dongarwar, Luyao Chen, and Xiaoqian Jiang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220702150840598570522},
  booktitle={Journal of Biomedical Informatics},
  volume={125},
  number={103974},
  year={2022},
}
Lishan Yu, Hamisu M. Salihu, Deepa Dongarwar, Luyao Chen, and Xiaoqian Jiang. Deep graph convolutional network for US birth data harmonization. 2022. Vol. 125. In Journal of Biomedical Informatics. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220702150840598570522.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved