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Geo-SPS: bipartite graph representation for GeoSpatial prenatal survey data

  • S.I. : Deep Geospatial Data Understanding
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Abstract

Obstetric studies had long revealed that the human female mental state, although subjective, has a nonlinear relation to the gestation, which could eventually leads to eugenics characteristics. Due to the difference of regions, there are differences between the data, people want to analyze the correlation between the survey data of different regions. Traditional obstetric studies explore health information on understanding and predicting this psychological state. However, most traditional research is based on statistical methods that exploring the correlation between numbers. This type of method lacks an understanding of the natural semantics of obstetric data, so it is prone to problems such as deviation or missing data (such as missing geospatial information) in data analysis. To tackle this problem, we study the use of a generic graph representation on gynecology and obstetric surveys with geospatial features, and propose a bipartite approach, or Geo-SPS, to mine the semantic relationship between low quality health information data. Our solution highlights our unconventional adaptation of novel graph theory from computational physics into biomedical ontology, that carefully maps semantic objects into a bipartite graph. With this tool, Geo-SPS provides a unique approach to semantic similarity metrics. The method supports fast and understandable processing of mixed textual data under different geographic spaces in a graph convolutional neural network. We further evaluate and validate the feasibility of Geo-SPS using a case study on obstetric surveys and health information data from over 3000 pregnant women in three different places from China. Results show that Geo-SPS can effectively represent prenatal mental state from this mixed data set, with an accurate classification on birth defects.

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Funding

Funding

Funding was provided by key technologies research and development program (Grant No. 2018YFB1404303) and ICT Grant CARCHB202017.

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  1. J. Cheng and L. Lian are contribute equally for this work.

Authors and Affiliations

  1. Nanchang University, Jiangxi Technical College of Manufacturing, Nanchang, Jiangxi, China

    Jie Cheng

  2. The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China

    Lu Lian

  3. Nanchang University, Nanchang, Jiangxi, China

    Zichen Xu, Dan Wu & Yuhao Wang

  4. Institute of Systems Engineering, Beijing, China

    Haoyang Zhu & Xiao Sun

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  1. Jie Cheng
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Correspondence to Zichen Xu or Dan Wu.

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We wish to submit this original research article entitled “Geo-SPS: Bipartite Graph Representation for GeoSpatial Prenatal Survey Data” for the consideration by Neural Computing and Applications. We confirm that this work is an original research paper. All authors have contributed on proofreading this draft. We have no conflict of interest on this original manuscript.

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Cheng, J., Lian, L., Xu, Z. et al. Geo-SPS: bipartite graph representation for GeoSpatial prenatal survey data. Neural Comput & Applic 35, 3709–3724 (2023). https://doi.org/10.1007/s00521-021-06371-2

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