Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques

Abstract Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions among factors limits the use of conventional statistical methods. This study aimed to establish a simple and accurate predictive model for COVID-...

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Main Authors: Takuya Ozawa, Shotaro Chubachi, Ho Namkoong, Shota Nemoto, Ryo Ikegami, Takanori Asakura, Hiromu Tanaka, Ho Lee, Takahiro Fukushima, Shuhei Azekawa, Shiro Otake, Kensuke Nakagawara, Mayuko Watase, Katsunori Masaki, Hirofumi Kamata, Norihiro Harada, Tetsuya Ueda, Soichiro Ueda, Takashi Ishiguro, Ken Arimura, Fukuki Saito, Takashi Yoshiyama, Yasushi Nakano, Yoshikazu Muto, Yusuke Suzuki, Ryuya Edahiro, Koji Murakami, Yasunori Sato, Yukinori Okada, Ryuji Koike, Makoto Ishii, Naoki Hasegawa, Yuko Kitagawa, Katsushi Tokunaga, Akinori Kimura, Satoru Miyano, Seishi Ogawa, Takanori Kanai, Koichi Fukunaga, Seiya Imoto
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
COVID-19
Machine learning
Artificial intelligence
Online Access:https://doi.org/10.1038/s41598-025-85733-5
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https://doi.org/10.1038/s41598-025-85733-5

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