Japanese / English

Detail of Publication

Text Language Japanese
Authors Hina Kotoura, Kohei Yamamoto, Koki Horikawa, Ryohei Takamoto, Yuta Nonomiya, Yuichiro Iwashita, Kaori Kuriu, Shoya Ishimaru, Soichiro Nakako, Hiroshi Okamura, Masakazu Iwamura, Koichi Kise, Ayumi Shintani
Title Tree-based machine learning model integrating clinical table data and chest x-ray images to predict prognosis of covid-19 patients
Journal Proceedings of the 43rd Joint Conference on Medical Informatics (the 24th conference of the Japan Association for Medical Informatics)
Presentation number 4-G-4-03
Number of Pages 5 pages
Publisher 日本医療情報学会
Location 神戸ファッションマート
Reviewed or not Reviewed
Presentation type Oral
Month & Year November 2023
Abstract Background: Since the peak number of COVID-19 mortality in Japan tends to increase with each outbreak, it is important to identify patients at high risk of death at an early stage. In a previous study, a neural network model was simultaneously trained with clinical and imaging information to predict the prognosis of COVID-19 patients. On the other hand, it has been reported that tree-based models outperform neural networks for table data. We aimed to construct and evaluate a tree-based prognostic model that treats chest X-ray images in the same way as other clinical information by reducing them to one dimension using neural networks. Methods: This study used the Stony Brook University COVID-19 dataset of 1313 inpatients. In order to predict mortality of all patients, we developed a model (hereafter referred to as “proposed model”) with LightGBM trained with the clinical table data combined with the result of prediction of a model with DenseNet trained with chest radio graphs. We also developed a model with LightGBM trained with the clinical data alone and a model with DenseNet trained with images. Results: The f1 scores for the proposed model, the table data-only model, and the image-only model were 0.59, 0.58, and 0.25, respectively. The ROC-AUC for the proposed model, the table data-only model, and the image-only model were 0.91, 0.93, and 0.63, respectively. Image prediction had the highest variable importance in the proposed model. Conclusion: the proposed model achieved high prediction accuracy. It was suggested that clinical table data contributed more to than chest X-ray images. On the other hand, images may improve prediction accuracy when combined with clinical table data.
URL https://confit.atlas.jp/guide/event/jcmi2023/subject/4-G-4-03/entries
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