Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.

Journal: Diabetes & metabolism journal
PMID: 38685670

Abstract

BACKGRUOUND: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Authors

  • Chuan Yun
    Department of Endocrinology, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
  • Fangli Tang
    International School of Nursing, Hainan Medical University, Haikou, China.
  • Zhenxiu Gao
    School of International Education, Nanjing Medical University, Nanjing, China.
  • Wenjun Wang
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China.
  • Fang Bai
    Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, China.
  • Joshua D Miller
    Department of Medicine, Division of Endocrinology & Metabolism, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Huanhuan Liu
    Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China.
  • Yaujiunn Lee
    Lee's United Clinic, Pingtung City, Taiwan.
  • Qingqing Lou
    The First Affiliated Hospital of Hainan Medical University, Hainan Clinical Research Center for Metabolic Disease, Haikou, China.

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