Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: This disclosure describes systems, devices, and techniques for identifying sections of medical documents that are suitable for automated medical coding. In one example, a computer-implemented method includes receiving, by one or more processors, the medical document, wherein the medical document comprises a plurality of sections. The method also may include determining, by the one or more processors and via application of a classification model to each section of the plurality of sections, codability indicia for each section of the plurality of sections, wherein the codability indicia represents whether the respective section is suitable for automated medical coding. The method may include outputting, by the one or more processors, the respective codability indicia for each section of the plurality of sections.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving historical activities of a plurality of users, the historical activities comprising actions associated with the items in the database, the plurality of users including the target user. A group of users is identified from the plurality of users based on historical activities of the group of users and historical activities of the target user. Generally current time activities of the group of users are received, the generally current time activities comprising activities that have occurred within a defined time window. A recommendation for one or more items is generated based on the generally current time activities, and the recommendation is provided for display to the target user.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: This disclosure describes systems, devices, and techniques for identifying sections of medical documents that are suitable for automated medical coding. In one example, a computer-implemented method includes receiving, by one or more processors, the medical document, wherein the medical document comprises a plurality of sections. The method also may include determining, by the one or more processors and via application of a classification model to each section of the plurality of sections, codability indicia for each section of the plurality of sections, wherein the codability indicia represents whether the respective section is suitable for automated medical coding. The method may include outputting, by the one or more processors, the respective codability indicia for each section of the plurality of sections.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Abstract: In one example embodiment, a method is illustrated as including retrieving item data. At least one base cluster having at least one document with common item data stored in a suffix ordering is constructed. The at least one base cluster is compacted to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters.

Abstract: In one example embodiment, a method is illustrated as including retrieving item data from a plurality of listings, the item data filtered from noise data, constructing at least one base cluster having at least one document with common item data stored in a suffix ordering, compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters, and merging the compacted cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data.

Abstract: In one example embodiment, a method is illustrated as including retrieving item data from a plurality of listings, the item data filtered from noise data, constructing at least one base cluster having at least one document with common item data stored in a suffix ordering, compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters, and merging the compacted cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: In one example embodiment, a method is illustrated as including retrieving item data from a plurality of listings, the item data filtered from noise data, constructing at least one base cluster having at least one document with common item data stored in a suffix ordering, compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters, and merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.

Abstract: Embodiments of computer-implemented methods and systems for activity-based recommendations are described. One example embodiment includes receiving data indicating historical activities of a user community, the historical activities including historical activities of the target user, selecting a reference group of users from the user community based on analysis of the historical activities of the target user, receiving generally current time activities of the reference group of users, the generally current time activities including those activities that have occurred within a defined time window, and recommending items to the target user based on the generally current time activities of the reference group of users.