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Clinical Trial Summary

The fracture risk of diabetic patients proves to be higher than those without diabetesdue to thehyperglycemia, usage of diabetes drugs, the changes in insulin levels and excretion, and this risk begins as early as adolescence.Many factors may be related to bone metabolism in patients with diabetes, including demographic data (e.g. age, height, weight, gender), medical history (e.g. smoking, drinking, menopause) and examination (e.g. bone mineral density, blood routine), urine routine).However, most of existing methods are qualitative assessments and do not take the interactions of the physiological factors of humans into consideration. In addition, the fracture risk of diabetic patients with osteoporosis has not been further studied before. In order to investigate the effect of patients' physiological factors on fracture risk, in the paper, we used a hybrid model combining XGBoost with deep neural network to predict the fracture risk of diabetic patients with osteoporosis.


Clinical Trial Description

n/a


Study Design


Related Conditions & MeSH terms

  • Healthcare; Risk Prediction; Diabetic Patients With Osteoporosis
  • Osteoporosis

NCT number NCT04534166
Study type Observational [Patient Registry]
Source Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Contact
Status Enrolling by invitation
Phase
Start date July 1, 2012
Completion date September 30, 2022