Abstract
PURPOSE OF REVIEW: To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature.
RECENT FINDINGS: Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.
Original language | English |
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Journal | Current Osteoporosis Reports |
Volume | 16 |
Issue number | 3 |
Pages (from-to) | 320–324 |
Number of pages | 5 |
ISSN | 1544-1873 |
DOIs | |
Publication status | Published - Jun 2018 |
Keywords
- Big data
- Diabetes
- Fractures
- Glucose
- Machine learning
- Osteoporosis