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A Model for Risk Prediction of Fracture in Diabetic Patients With Osteoporosis

NCT04534166 · Status: UNKNOWN · Type: OBSERVATIONAL

Last updated 2020-09-01

No results posted yet for this study

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.

Conditions

  • Healthcare; Risk Prediction; Diabetic Patients With Osteoporosis

Interventions

OTHER

Risk Prediction of Fracture in Diabetic Patients with Osteoporosis

Risk Prediction of Fracture in Diabetic Patients with Osteoporosis

Sponsors & Collaborators

  • Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2012-07-01
Primary Completion
2022-09-30
Completion
2022-09-30

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04534166 on ClinicalTrials.gov