Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.
10.1007/s11684-021-0828-7
- Author:
Yaxin CHEN
1
;
Tianyi YANG
2
;
Xiaofeng GAO
3
;
Ajing XU
4
Author Information
1. Department of Pharmacy, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China.
2. Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
3. Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. gao-xf@cs.sjtu.edu.cn.
4. Department of Pharmacy, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China. xuajing@xinhuamed.com.cn.
- Publication Type:Journal Article
- Keywords:
XGBoost;
deep neural network;
healthcare;
risk prediction
- MeSH:
Bone Density;
Deep Learning;
Diabetes Mellitus/epidemiology*;
Female;
Fractures, Bone/etiology*;
Humans;
Osteoporosis/complications*;
Risk Factors
- From:
Frontiers of Medicine
2022;16(3):496-506
- CountryChina
- Language:English
-
Abstract:
The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients' physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.
