Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

Journal: Diabetes research and clinical practice
PMID: 39428040

Abstract

AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease (CKD) and to pinpoint key modifiable risk factors for targeted interventions.

Authors

  • Woo Vin Lee
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea. Electronic address: wu6in@snu.ac.kr.
  • Yuri Song
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
  • Ji Sun Chun
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
  • Minoh Ko
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
  • Ha Young Jang
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; College of Pharmacy, Gachon University, 191, Hambangmoe-ro, Yeonsu-gu, Incheon 21565, Republic of Korea.
  • In-Wha Kim
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
  • Sehoon Park
    Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
  • Hajeong Lee
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Hae-Young Lee
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
  • Soo Heon Kwak
    Department of Internal Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. Electronic address: shkwak@snu.ac.kr.
  • Jung Mi Oh
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; College of Pharmacy, Natural Products Research Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea. Electronic address: jmoh@snu.ac.kr.

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