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. 2024 Jun 13;19(6):e0304106.
doi: 10.1371/journal.pone.0304106. eCollection 2024.

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

Hyeon-Ju Jeon  1 , Hyeon-Jin Jeon  2 , Seung Ho Jeon  3
Affiliations

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

Hyeon-Ju Jeon et al. PLoS One. .

Abstract

Air pollution causes and exacerbates allergic diseases including asthma, allergic rhinitis, and atopic dermatitis. Precise prediction of the number of patients afflicted with these diseases and analysis of the environmental conditions that contribute to disease outbreaks play crucial roles in the effective management of hospital services. Therefore, this study aims to predict the daily number of patients with these allergic diseases and determine the impact of particulate matter (PM10) on each disease. To analyze the spatiotemporal correlations between allergic diseases (asthma, atopic dermatitis, and allergic rhinitis) and PM10 concentrations, we propose a multi-variable spatiotemporal graph convolutional network (MST-GCN)-based disease prediction model. Data on the number of patients were collected from the National Health Insurance Service from January 2013 to December 2017, and the PM10 data were collected from Airkorea during the same period. As a result, the proposed disease prediction model showed higher performance (R2 0.87) than the other deep-learning baseline methods. The synergic effect of spatial and temporal analyses improved the prediction performance of the number of patients. The prediction accuracies for allergic rhinitis, asthma, and atopic dermatitis achieved R2 scores of 0.96, 0.92, and 0.86, respectively. In the ablation study of environmental factors, PM10 improved the prediction accuracy by 10.13%, based on the R2 score.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. An architecture of the proposed disease prediction model.
n and m indicate the length of observation sequences and prediction sequences of the temporal model, respectively.
Fig 2
Fig 2. A comparison of the prediction performance in MST-GCN with and without PM10 concentrations in the input data.
X-axes of the plots refer to each disease, and Y-axes correspond to the evaluation metrics. (a)-(e) indicate the five evaluation metrics.
See this image and copyright information in PMC

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