Internal Medicine

makino masaki

  (牧野 真樹)

Profile Information

Affiliation
School of Medicine Faculty of Medicine, Fujita Health University
Degree
博士(医学)

J-GLOBAL ID
201501006823621048
researchmap Member ID
7000012783

Awards

 1

Papers

 51
  • 垣田 彩子, 牧野 真樹, 小出 晴香, 初野 麻佑, 重康 裕紀, 川上 司, 公文 尚子, 平塚 いづみ, 植田 佐保子, 四馬田 恵, 清野 祐介, 高柳 武志, 鈴木 敦詞
    日本内分泌学会雑誌, 99(2) 625-625, Oct, 2023  
  • 川上 司, 牧野 真樹, 西田 康貴, 重康 裕紀, 吉木 優, 宮島 桂一, 坂口 英林, 尾崎 行男, 鈴木 敦詞
    糖尿病, 66(Suppl.1) S-293, Apr, 2023  
  • Eiichiro Kanda, Atsushi Suzuki, Masaki Makino, Hiroo Tsubota, Satomi Kanemata, Koichi Shirakawa, Toshitaka Yajima
    Scientific reports, 12(1) 20012-20012, Nov 21, 2022  
    Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan-Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.
  • 重康 裕紀, 牧野 真樹, 川上 司, 西田 康貴, 松尾 悠志, 吉野 寧維, 清野 祐介, 鈴木 敦詞
    日本内分泌学会雑誌, 98(2) 612-612, Oct, 2022  
  • 中島 優華, 川上 司, 戸松 瑛介, 牧野 真樹, 淺田 陽平, 清野 祐介, 鈴木 敦詞
    糖尿病, 65(4) 202-202, Apr, 2022  

Misc.

 213

Books and Other Publications

 1

Presentations

 53