Estimation of Bone Mineral Density Using Machine Learning Approach

AbstractOsteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.

Download Full-text

Machine Learning Reveals Influence of Stool pH on Bone Mineral Density in a Healthy Multi-Ethnic U.S. Population

Current Developments in Nutrition ◽

10.1093/cdn/nzab033_008 ◽

2021 ◽

Vol 5 (Supplement_2) ◽

pp. 8-8

Author(s):

Elizabeth Chin ◽

Marta Van Loan ◽

Sarah Spearman ◽

Ellen Bonnel ◽

Kevin Laugero ◽

...

Keyword(s):

Machine Learning ◽

Bone Mineral Density ◽

Bone Mineral ◽

Bone Health ◽

Whole Body ◽

Fiber Intake ◽

Learning Models ◽

Mineral Density ◽

Modifiable Factors ◽

Machine Learning Models

Abstract Objectives A variety of modifiable and non-modifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. The objective of this study was to use dietary, physiological, and lifestyle data to identify directly modifiable and non-modifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy US men and women using machine learning models. Methods Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy US adults (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (nutrient or food groups intake via food frequency questionnaire) variables, and variables approximating directly modifiable behavior (circulating vitamin D and stool pH). Model feature importances were used to identify variables useful for predicting BMC and BMD. Results Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R2 = 0.750) compared to when using only directly modifiable variables (highest R2 = 0.107). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R2 = 0.063) when using directly modifiable variables only. BMI, body fat %, height, and menstruation history were predictors of BMC and BMD. For the directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. Conclusions Machine learning models can be used to identify previously unforeseen variables that may contribute to bone health. Modifiable factors explained less variation in the data compared to other features. Low stool pH, which has been shown to be associated with fermentable fiber intake, short chain fatty acid production, and enhanced calcium absorption, was associated with higher BMC and BMD in a healthy US population. Funding Sources USDA-ARS

Download Full-text

Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears

Risk Management and Healthcare Policy ◽

10.2147/rmhp.s330555 ◽

2021 ◽

Vol Volume 14 ◽

pp. 3977-3986

Author(s):

Tong-Fu Wang ◽

De-Sheng Chen ◽

Jia-wang Zhu ◽

Bo Zhu ◽

Zeng-Liang Wang ◽

...

Keyword(s):

Machine Learning ◽

Bone Mineral Density ◽

Rotator Cuff ◽

Medical Care ◽

Clinical Features ◽

Bone Mineral ◽

Rotator Cuff Tears ◽

Mineral Density ◽

Medical Care Costs ◽

Care Costs

Download Full-text

Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men

Scientific Reports ◽

10.1038/s41598-021-83828-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Qing Wu ◽

Fatma Nasoz ◽

Jongyun Jung ◽

Bibek Bhattarai ◽

Mira V. Han ◽

...

Keyword(s):

Machine Learning ◽

Bone Mineral Density ◽

Linear Regression ◽

Bone Mineral ◽

Genomic Data ◽

Genotype Imputation ◽

Gradient Boosting ◽

Mineral Density ◽

Phenotypic Data ◽

Individual Snps

AbstractThe study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models’ performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data.

Download Full-text