The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis

Current Osteoporosis Reports
Christian Kruse

Abstract

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. 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.

References

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Citations

May 3, 2019·Current Opinion in Rheumatology·Uran FeriziGregory Chang

Related Concepts

Machine Learning
Active Machine Learning
Unsupervised Machine Learning
Blood Glucose
Skeletal Bone
Diabetes Mellitus
Optical Image Reconstruction
Post-Traumatic Osteoporosis
Diabetes
Diabetes Mellitus

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