Abstract: One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying, using at least one trained machine-learning model, at least a portion of the extracted conceptual units into one of a plurality of classification groups; storing the classified extracted conceptual units into a data repository as defined by the schema; displaying, within a user interface on a display of an information handling device, a digital standard in a format based upon the schema; and providing, within the user interface, search and filter functions allowing for finding information related to the digital standard. Other aspects are described and claimed.

Abstract: One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying, using at least one trained machine-learning model, at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein each of the classification groups identifies a function of the extracted conceptual units, included within a given classification group, within the underlying standard; wherein the classifying comprises classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; and storing the classified extracted conceptual units into a data repository based upon the schema. Other aspects are described and claimed.

Abstract: One embodiment provides a method for training a machine-learning model to detect a location of a person's appendix, comprising: receiving, at the machine-learning model, a plurality of images, each image being a slice of a body taken by a CT scan; identifying, for each of the plurality of images, features of the appendix, wherein the identifying comprises analyzing a plurality of slices of each of the plurality of images and classifying each of the plurality of slices, into one of a plurality of classification groups, based upon a feature of the appendix within the slice; segmenting each of the plurality of image slices included in the one of the plurality of classification groups that classifies the slice as containing the appendix, thereby identifying probable locations of the appendix, via utilizing a probability mask for each of the probable locations.

Abstract: One embodiment provides a method for generating a digital standard utilizing a trained machine-learning model, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying, using at least one trained machine-learning model, at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein each of the classification groups identifies a function of the extracted conceptual units, included within a given classification group, within the underlying standard; wherein the classifying comprises classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; and storing the classified extracted conceptual units into a data repository based upon the schema. Other aspects are described and claimed.