Hiroyuki Uchinuma, Kyoichiro Tsuchiya, Sayaka Horiuchi, Megumi Kushima, Sanae Otawa, Hiroshi Yokomichi, Kunio Miyake, Yuka Akiyama, Tadao Ooka, Reiji Kojima, et al. Advanced maternal age elevates the prevalence of hypertensive disorders in women of Japanese, independent of blood pressure: a study from the Japan Environment and Children’s study. Hypertension Research. 2024
Satoshi Shinohara, Ryoji Shinohara, Reiji Kojima, Sanae Otawa, Megumi Kushima, Kunio Miyake, Hideki Yui, Tadao Ooka, Yuka Akiyama, Sayaka Horiuchi, et al. Neonatal transfer and duration of hospitalization of newborns as potential risk factors for impaired mother-infant bonding: The Japan Environment and Children's Study. Journal of Affective Disorders. 2024. 360. 314-321
Tadao Ooka, Naoto Usuyama, Ryohei Shibata, Michihito Kyo, Jonathan M Mansbach, Zhaozhong Zhu, Carlos A Camargo Jr, Kohei Hasegawa. Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis. NPJ systems biology and applications. 2024. 10. 1. 93-93
Michihito Kyo, Zhaozhong Zhu, Ryohei Shibata, Tadao Ooka, Jonathan M Mansbach, Brennan Harmon, Andrea Hahn, Marcos Pérez-Losada, Carlos A Camargo, Kohei Hasegawa. Nasal microRNA signatures for disease severity in infants with respiratory syncytial virus bronchiolitis: a multicentre prospective study. BMJ open respiratory research. 2024. 11. 1
Satoshi Shinohara, Sayaka Horiuchi, Reiji Kojima, Ryoji Shinohara, Sanae Otawa, Megumi Kushima, Kunio Miyake, Hideki Yui, Tadao Ooka, Yuka Akiyama, et al. Maternal excessive weight gain as a potential risk factor for prolonged labor in Japanese pregnant women: The Japan Environment and Children’s Study. PLOS ONE. 2024. 19. 7. e0306247-e0306247
Artificial Intelligence Approaches to Type 2 Diabetes Risk Prediction and Exploration of Predictive Factors
(IEA WORLD CONGRESS OF EPIDEMIOLOGY 2021 2021)
機械学習を活用した将来の健康診断検査値の予測方法の検討
(第31回日本疫学会学術総会 2021)
Deep Learningを活用して健康診断結果から糖尿病発症を予測する方法の検討
(第79回日本公衆衛生学会総会 2020)