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Recent patent applications related to

Healthcare provider claims denials prevention systems and methods

An example system includes a memory, processor, and instructions to receive a set of multidimensional adjudicated claims data, receive metric and lens functions, perform the metric and lens functions on a set of dimensions of the claims data to map claims to a reference space, generate cover of overlapping sets of the reference space, cluster the mapped claims in the reference space using the cover to identify nodes and edges, identify groups of nodes in a graph based on known improperly denied, for each group, identify differentiating drivers, and generate a denials application user interface depicting different cards for each of at least a subset of the identified groups in the graph that includes the known improperly denied claims, each card indicating a set of primary statistics of the claims in the nodes of that group, for each card depicting the differentiating drivers of that group.. . ... Ayasdi Inc

Automated discovery using textual analysis

An example method includes receiving text from a plurality of documents, segmenting text received text of the plurality of documents, calculating a frequency statistic for each segment of each document, determining segments of potential interest of each document based on calculated frequency statistic, calculating distances between each document of the plurality of documents based on a text metric, and storing segments of potential interest of each document and the distances in a search database. The method may further include receiving a search query and performing a search of information contained in the search database, partitioning documents of search results using the distances, for each partition, determining labels of segments of potential interest for documents of that particular partition, the labels being determined based on a plurality of frequency statistics, and providing determined labels of segments of potential interest for documents of each partition.. ... Ayasdi Inc

Query capabilities of topological data analysis graphs

A method comprises receiving a data set, mapping data points from the data set to a reference space utilizing a lens function, generating a cover of the reference space using a resolution function, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes of a graph, generating a graph including the plurality of nodes, the graph including an edge between every two nodes that share at least one data point as a member, and generating first and second data structures, the first data structure identifying membership of each node, the second data structure identifying each edge between each of the two nodes, the second data structure further identifying the nodes that are connected by each edge, the first and second data structure being capable of being queryable using a query language.. . ... Ayasdi Inc

Scalable topological data analysis using topological summaries of subsets

A method comprises dividing a set of data points into a structure subset and boost subsets, adding the data points in structure subset into each boost subset, analyzing the structure subset using topological data analysis (tda) to identify nodes of a structure graph, boost graph, and modified graph, analyze each of the boost subsets using the tda to identify additional nodes of boost graph, for each node in each of the plurality of boost graphs that do not share at least one data point with a node in the structure graph, adding the node of a particular boost subset including data points that are members of the node, to the modified graph, and generating report indicating relationships between data points of the set of data points based on the nodes of the modified graph.. . ... Ayasdi Inc

Topological data analysis of data from a fact table and related dimension tables

A method comprises receiving a selection of data from a fact table and one or more dimension tables stored in a data warehouse, mapping data points from the selection of the data from the fact table and the one or more dimension tables to a reference space utilizing a lens function, generating a cover of the reference space using a resolution function, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes of a graph, each node including at least one data point, determining a plurality of segments of the graph, each segment including at least one node, and generating a segment data structure identifying each segment as well as membership of each segment, the membership of each segment including at least one node from the plurality of nodes in the graph.. . ... Ayasdi Inc

Topological data analysis utilizing spreadsheets

A method comprises receiving data points from a spreadsheet, mapping the data points to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space to determine each node of a graph, each node including at least one data point, generating a visualization depicting the nodes, the visualization including an edge between every two nodes that share at least one data point, generating a translation data structure indicating location of the data points in the spreadsheet as well as membership of each node, detecting a selection of at least one node, determining the location of data points in the spreadsheet corresponding to data points that are members of the selected node(s) using the translation data structure, and providing a first command to a spreadsheet application to provide a first visual identification of the first set of data points in the spreadsheet.. . ... Ayasdi Inc

Scalable topological data analysis

An example method comprises receiving first data associated with data points, receiving a lens function selection, a metric function selection, and a resolution function, the metric function identified by the metric function selection being capable of performing functions on data as matrix functions, mapping second data based on the first data to a reference space by utilizing matrix vector multiplication for application of selected lens function on second data based on the first data to map the second data to the reference space, generating cover of reference space including the second data, clustering second data in cover based on the selected metric function to determine each node of a plurality of nodes, each of the nodes of the plurality of nodes comprising members representative of at least one subset of the data points, and generating a visualization comprising the plurality of nodes and a plurality of edges wherein each of the edges of the plurality of edges connects nodes with shared members.. . ... Ayasdi Inc

An example method includes receiving a first set of data identifying entities and performance information for analysis, receiving a second set of data identifying entities and performance information associated with known or suspected past fraud or abuse, receiving metric and lens selections, performing metric and lens functions based on the metric and lens selections on first and second set of data, generating cover of reference space and cluster mapped performance information to identify nodes in a graph, each node including one or more entities as members, each node being connected to another node if they share at least one common entity as members, identifying nodes that include at least one member from the second set of data, determining entities that are members of the identified nodes that are from the first set of data, and generating a first report listing the determined entities as possibly involved in fraud or waste.. . ... Ayasdi Inc

Network representation for evolution of clusters and groups

An example method includes receiving a data set, each data point in the data set being associated with an indication of time, and a distance function, determining overlapping intervals over a time period range, identifying subsets of data in each overlapping interval based on the indications of time, applying the distance function to each subset of data to identify groups, constructing a node for each group to create a plurality of nodes, determining if two nodes of the plurality of nodes in adjacent time periods are connected by scoring shared data point membership between the two nodes and comparing a score of the shared data point membership to a threshold, and displaying at least two nodes with an indication of time, the two nodes being connected by a line based on the comparison of the score and the threshold.. . ... Ayasdi Inc

Topological data analysis for identification of market regimes for prediction

An example method includes receiving a data set, generating a topological representation using topological data analysis, at least one metric-lens combination, and the data set, the representation including a plurality of nodes, each of the nodes having one or more data points as members, receiving a new data point, determining distances between the new data point and at least some of the one or more data points, locating the new data point in a location relative to one or more of the nodes using the distances, identifying a subset of the data points closest to the location of the new data point, comparing the subset of the data points to at least some information regarding the new data point to identify a regime, and generating a report indicating a model associating factors associated with the subset of the data points with the new data point for predicting future outcomes.. . ... Ayasdi Inc

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