ABSTRACT
The data analysis highlights the ability of big data and advanced artificial intelligence (AI) to generate actionable insights that are impossible to obtain with more traditional statistical analyses or manual evaluation of patient records. The term deep learning is a subset of machine learning (ML) that corrects its own errors, learning from its mistakes, using layers and back propagation in an artificial neural network, for instance. Neural networks are designed to mimic the functioning of the human nervous system, with its neurons and synapses. Although convolutional neural networks, random forest modeling, and related technology are the foundation upon which the latest ML-based algorithms are built, to understand these constructs first requires that one be familiar with AI terminology; it likewise requires an awareness that these terms are often defined differently by different experts. Gradient boosting has proven useful in sifting through data on patients with prediabetes to estimate which ones will most likely develop full-blown diabetes.
