Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

Methods and systems for detecting a vertebral fracture within an x-ray. One method includes receiving a chest x-ray image and identifying a plurality of vertebrae represented in the chest x-ray image. The method further includes extracting a plurality of image patches from the chest x-ray image, each image patch of the plurality of image patches including a portion of the chest x-ray image representing one of the plurality of vertebrae identified in the chest x-ray image. The method further includes sequencing the plurality of image patches into an ordered sequence of image patches, and assigning, with a deep learning model applied to the ordered sequence of image patches, a classification to each of the plurality of image patches indicating whether the image patch represents a fractured vertebra or an unfractured vertebra.

A predictor has a memory which stores at least one example for which an associated outcome is not known. The memory stores at least one decision tree comprising a plurality of nodes connected by edges, the nodes comprising a root node, internal nodes and leaf nodes. Individual ones of the nodes and individual ones of the edges each have an assigned module, comprising parameterized, differentiable operations, such that for each of the internal nodes the module computes a binary outcome for selecting a child node of the internal node. The predictor has a processor configured to compute the prediction by processing the example using a plurality of the differentiable operations selected according to a path through the tree from the root node to a leaf node.

Implementations described here provide a chatbot system that is configurable to meet the needs of the user. In one implementation, a chatbot system is configured to utilize and vary decision trees, decision tree complexity, and decision tree selection adaptive to the needs of the end user. Further, implements may utilize decision trees that are configured as static or dynamic using independent leaves and skillsets to allow for variations in the level of sophistication needed for a chatbot conversation. In other implementations, a chatbot system may be configured to assess the chatbot scenario and requirements in order to adapt processing requirements in order to increase or decrease processing threads to vary processing efficiency relative to the needs of the user. In some scenarios, the chatbot system described herein is introspective thereby using feedback and data to be adaptive to chatbot session errors and self-healing.

Methods, systems, and computer-readable storage media for receiving historical data, the historical data including variable vectors, each variable vector being assigned to a class, processing the historical data through encoders to provide feature vectors, each feature vector corresponding to a respective variable vector and being assigned to the class of the respective variable vector, generating a set of decision trees based on the feature vectors, each decision tree corresponding to a class in the set of classes, transforming each decision tree into a set of rules to provide sets of rules, each rule in a set of rules defining conditions to assign at least a portion of an electronic document to a respective class in the set of classes, and providing the sets of rules for execution in an enterprise system, the enterprise system classifying electronic documents to classes in the set of classes based on the sets of rules.

The present disclosure uses automated, computer-driven analysis of video feeds to extract raw motion metadata, identify moving objects and assess whether or not the moving objects are compliant with certain predefined rules. The motion can be visually plotted on a background in the form of motion trails, and a chronological plot of the motion may be provided as a menu to find anomalies through visual screening. The motion metadata may be filtered through annotations. A highlighted portion of the video feed relating to a specific topic of interest may be replayed.

Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Systems and methods that allow examination of response data collected from content providers and provide for classification and routing according to the classification. The process of classification employs an unsupervised, or partially unsupervised, Machine Learning classifier model for identifying data collection responses that contains no data, mangled data, or a block, for assigning a classification correspondingly and for feeding the classification decision back to a data collection platform.

Systems and methods are described for providing an interface and implementing rules and metrics received from the interface regarding the selection of sponsored content networks that provide sponsored content items. This may include providing mediation code to a publisher for inclusion in publisher content provided to a user device, the mediation code associated with a table of sponsored content networks, receiving an image of a sponsored content item and a sponsored content network identifier from the user device, analyzing the image of the sponsored content item, the analysis generating extracted image data from the sponsored content item, categorizing the sponsored content item based on the extracted image data and the sponsored content network identifier, receiving an instruction to filter the sponsored content networks exceeding a metric based on a category, and updating the table of sponsored content networks to remove a particular sponsored content network.

Systems, methods, and techniques to efficiently analyze and navigate through decision logic using an execution graph are provided. The method includes executing decision logic in response to receiving a data file. The method further includes generating, in response to the executing, an execution graph. The execution graph includes a plurality of nodes corresponding to a plurality of decision entities of the decision logic. The method further includes displaying the execution graph on a user interface. The method further includes displaying, in response to receiving a selection of a node of the plurality of nodes, information associated with the selected node.