Machine Learning

  • Inoue K, Athey S, Tsugawa Y. (2023). Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management. International Journal of Epidemiology.52(4):1243-1256.
  • Inoue K, Nianogo R, Telesca D, Goto A, Khachadourian V, Tsugawa Y, Sugiyama T, Mayeda ER, Ritz B. (2020). Low HbA1c levels and all-cause or cardiovascular mortality among people without diabetes: the US National Health and Nutrition Examination Survey 1999-2015. International Journal of Epidemiology.50(4),1373-1383.
  • Inoue K, Seeman T, Horwich T, Budoff M, Watson KE (2022). Heterogeneity in the Association Between the Presence of Coronary Artery Calcium and Cardiovascular Events: A Machine Learning Approach in the MESA Study. Circulation.147(2):132-141.
  • Kato H, Hoshino Y, Hidaka N, Ito N, Makita N, Nangaku M, Inoue K. (2022). Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population. Journal of Clinical Endocrinology and Metabolism.107(12):3222-3230.
  • Yoshihara A, Yoshimura Noh J, Inoue K, et al (19 authors). (2022) Prediction model of Graves’ disease in general clinical practice based on complete blood count and biochemistry profile. Endocr J. 69(9):1091-1100.
  • Shiba K,Inoue K. (2024). Harnessing Causal Forests for Epidemiologic Research: Key Consideration. Am J Epidemiol. kwae003.