Disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics Data

Ji-Hyeong Park, Hyun-Soo Choi, Sunhwa Jo, Jinho Kim

2023

Abstract

Disease prediction is an important technology in the field of medicine. Several studies have been conducted on disease prediction using electronic health records (EHR). However, existing methods have several limitations, such as predicting only a single disease and utilizing limited data sources of textual or drug-related data; thus, they cannot capture the relationship between a patient and a disease, or among diseases. Furthermore, they suffer from the problem that additional information other than EHR exists only for a limited set of diseases and cannot be used for a wide range of diseases. To mitigate these problems, we utilize Toxicogenomics Data (TD) that contains extensive information about most diseases, and analyze this complicated data using a heterogeneous graph embedding technique. We utilize metapath and graph neural network for graph embedding of heterogeneous relationships in EHR-TD, and then develop a novel disease prediction framework. To achieve this goal, we first present a process for the collection and processing of EHR and TD data to improve their reliability. Secondly, we propose a method for efficiently constructing heterogeneous EHR-TD graphs, and present an embedding model that can be effectively used. Finally, we propose a metapath interaction encoder that can address the problems of RNN-based encoders in previous models. Thereafter, we validate the effectiveness of the proposed framework and modules with extensive evaluations of various designs for disease prediction using EHR and TD data.

Paper Citation

in Harvard Style

Park J., Choi H., Jo S. and Kim J. (2023). Disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics Data. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 97-104. DOI: 10.5220/0012096400003541

in Bibtex Style

@conference{data23,
author={Ji-Hyeong Park and Hyun-Soo Choi and Sunhwa Jo and Jinho Kim},
title={Disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics Data},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={97-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012096400003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics Data
SN - 978-989-758-664-4
AU - Park J.
AU - Choi H.
AU - Jo S.
AU - Kim J.
PY - 2023
SP - 97
EP - 104
DO - 10.5220/0012096400003541
PB - SciTePress