Abstract: Measurements of esophageal pressure and geometry are classified using a trained machine learning algorithm, such as a neural network or other classifier algorithm. Contractile response patterns can be identified in the esophageal pressure and geometry data, from which classified feature data can be generated. The classified feature data classify the esophageal pressure and geometry data as being indicative of an upper gastrointestinal disorder in the subject.

Abstract: Approaches presented herein provide systems and methods for a data platform to identify, evaluate, and map data for processing via one or more compute instances. The data platform may receive an instruction and retrieve data from a variety of remote data locations. The data may be processed, such as to label or file the data, and then streamed to a compute instance for further evaluation. The data platform may be used to provide a centralized system for managing system data that combine both legacy and modern storage solutions into a single integrated platform.