Abstract: Systems, methods, and apparatus providing a user interface to display clinical data and a clinical worklist are disclosed and described. An example system includes a memory to store instructions, data, and configuration information; and a processor to execute the instructions to generate a user interface. The example user interface includes at least a worklist area to display indicators associated with items to be opened for review, the items selectable to be opened or moved, the worklist area to be hidden or displayed based on a configuration of the user interface. The example user interface also includes a pinned area to display a copy of a selected subset of the items in the worklist area, the pinned area to remain visible when the worklist area is hidden and to persist across sessions of the user interface, wherein a data structure associated with the subset of the items is to be flagged.

Abstract: Systems, methods, and apparatus providing hierarchical data filtering are disclosed and described. An example system includes a hierarchical data filter including a plurality of levels including at least a parent level and a child level, wherein a parent filter is to apply to the parent level and the child level and a child filter is to apply to the child level. An example processor is to execute the instructions to at least: analyze data to match a type of the data to a level in the plurality of levels of the hierarchical data filter; apply one or more filters from the hierarchical data filter to the data to filter the data according to the corresponding level of the data from the hierarchical data filter; and output filtered data from the applying of the one or more filters to the data.

Abstract: A computer-implemented system and method for text classification is provided that applies a hybrid approach for text classification. The system and method includes a text pre-processor which prepares unclassified articles in a format which can be read by a two-stage classifier. The classifier employs a hybrid approach. A keyword-based model achieves machine-labelling of the articles. The machine-labelled articles are used to train a machine learning model. New articles can be applied against the trained model, and classified.