A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

Abstract

Authors

• Akira Okada
  Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
• Yohei Hashimoto
  Department of Ophthalmology, The University of Tokyo, Tokyo, Japan.
• Tadahiro Goto
  Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America. Electronic address: tag695@mail.harvard.edu.
• Satoko Yamaguchi
  Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
• Sachiko Ono
  Department of Eat-loss Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
• Kayo Ikeda Kurakawa
  Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
• Masaomi Nangaku
  Division of Nephrology and Endocrinology, The University of Tokyo Hospital, Bunkyo, Tokyo, Japan.
• Toshimasa Yamauchi
  Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
• Hideo Yasunaga
  Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo.
• Takashi Kadowaki
  Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Keywords

External Resources