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<p>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-19 severity using an explainable machine learning approach. A total of 3,301 patients ≥ 18 years diagnosed with COVID-19 between February 2020 and October 2022 were included. The discovery cohort comprised patients whose disease onset fell before October 1, 2020 (<i>N</i> = 1,023), and the validation cohort comprised the remaining patients (<i>N</i> = 2,278). Pointwise linear and logistic regression models were used to extract 41 features. Reinforcement learning was used to generate a simple model with high predictive accuracy. The primary evaluation was the area under the receiver operating characteristic curve (AUC). The predictive model achieved an AUC of ≥ 0.905 using four features: serum albumin levels, lactate dehydrogenase levels, age, and neutrophil count. The highest AUC value was 0.906 (sensitivity, 0.842; specificity, 0.811) in the discovery cohort and 0.861 (sensitivity, 0.804; specificity, 0.675) in the validation cohort. Simple and well-structured predictive models were established, which may aid in patient management and the selection of therapeutic interventions.</p>

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Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月19日

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  • 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

摘要

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-19 severity using an explainable machine learning approach. A total of 3,301 patients ≥ 18 years diagnosed with COVID-19 between February 2020 and October 2022 were included. The discovery cohort comprised patients whose disease onset fell before October 1, 2020 (N = 1,023), and the validation cohort comprised the remaining patients (N = 2,278). Pointwise linear and logistic regression models were used to extract 41 features. Reinforcement learning was used to generate a simple model with high predictive accuracy. The primary evaluation was the area under the receiver operating characteristic curve (AUC). The predictive model achieved an AUC of ≥ 0.905 using four features: serum albumin levels, lactate dehydrogenase levels, age, and neutrophil count. The highest AUC value was 0.906 (sensitivity, 0.842; specificity, 0.811) in the discovery cohort and 0.861 (sensitivity, 0.804; specificity, 0.675) in the validation cohort. Simple and well-structured predictive models were established, which may aid in patient management and the selection of therapeutic interventions.