Analyzing large quantities of raw complex data is often a challenge in the health care industry. During this webcast, there will be a demonstration of how unsupervised machine learning techniques can be applied to such data to help bring useful insights. This will include examples that use unsupervised learning techniques spanning dimensionality reduction, clustering and anomaly detection.
More advanced techniques, including Uniform Manifold Approximation and Projection (UMAP) and density-based spatial clustering, will also be presented by featuring a range of applications within the anomaly detection framework. These techniques will demonstrate how complex high-dimensional health care data can be tailored to give insights into provider utilization and help with affordability.