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 |
- Entry for BibTeX
@InCollection{Kotoura2023, author = {Hina Kotoura and Kohei Yamamoto and Koki Horikawa and Ryohei Takamoto and Yuta Nonomiya and Yuichiro Iwashita and Kaori Kuriu and Shoya Ishimaru and Soichiro Nakako and Hiroshi Okamura and Masakazu Iwamura and Koichi Kise and Ayumi Shintani}, title = {Tree-based machine learning model integrating clinical table data and chest x-ray images to predict prognosis of covid-19 patients}, booktitle = {Proceedings of the 43rd Joint Conference on Medical Informatics (the 24th conference of the Japan Association for Medical Informatics)}, year = 2023, month = nov, presenID = {4-G-4-03}, numpages = {5}, URL = {https://confit.atlas.jp/guide/event/jcmi2023/subject/4-G-4-03/entries}, publisher = {日本医療情報学会}, location = {神戸ファッションマート} }