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

Study Results

Results pending

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Basic Information

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Recruitment Status

UNKNOWN

Study Classification

OBSERVATIONAL

Study Start Date

2012-07-01

Study Completion Date

2022-09-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Healthcare; Risk Prediction; Diabetic Patients With Osteoporosis

Study Design

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Observational Model Type

ECOLOGIC_OR_COMMUNITY

Study Time Perspective

OTHER

Interventions

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Risk Prediction of Fracture in Diabetic Patients with Osteoporosis

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Patients in Hospital's outpatient and inpatient His database between July 2012 and November 2022, diabetic patients were combined with osteoporosis.

Exclusion Criteria

* Patients with fractures before diagnosis of diabetes; patients with fractures before diagnosis of osteoporosis; patients with hyroid disease and other diseases that seriously affect bone metabolism.

Eligible Sex

ALL

Accepts Healthy Volunteers

No