Optum Services (ireland) Limited

Optum, Inc.

The Provost, Fellows, Foundation Scholars, and the Other Members of Board, of the College of the Holy and Undivided Trinity of Queen Elizabeth, Near Dublin Trinity Collge Dublin

David S Monaghan

Kenneth Bryan

Chirag Chadha

Megan O'Brien

Brian Carter

Johnathon E Schultz

Aljosa A Smolic

Gregory Buckley

Jan Ondrej

Konstantinos Amplianitis

Ajay Ajit Maity

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a historically dynamic explanation data object for a dental image data object. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform generating a historically dynamic explanation data object for a dental image data object using an encoder-decoder architecture, where the encoder machine learning framework of the encoder-decoder architecture comprises a current diagnosis identification machine learning model, a historical diagnosis identification machine learning model, a convolutional embedding machine learning model, a new diagnosis code inference machine learning model, and a feature vector combination machine learning model.

Machine learning techniques for generating historically dynamic explanation data objects

There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Classification in hierarchical prediction domains

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, identifying object transformations in images and determining a sufficiency measure for an image set in capturing object transformations for one or more objects. In an embodiment, an example method comprises receiving a transformation record data entity and an image set comprising a plurality of images, the transformation record data entity comprising one or more object identifiers. The method further comprises identifying each object depicted by each image using an object identification machine learning and determining a sufficiency measure for the image set based at least in part on determining a transformation state for each object associated with one of the one or more object identifiers using one or more transformation classification machine learning models. The method further includes performing one or more automated sufficiency-based actions, which includes automatically generating and transmitting a request for supplemental image(s).

Automated object transformation identification

There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Cross-variant polygenic predictive data analysis

There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions can be processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

The method comprising providing a plurality of images of a scene captured by a plurality of image capturing devices (); providing silhouette information of at least one object in the scene (); generating a point cloud for the scene in 3D space using the plurality of images (); extracting an object point cloud from the generated point cloud, the object point cloud being a point cloud associated with the at least one object in the scene (); estimating a 3D shape volume of the at least one object from the silhouette information (); and combining the object point cloud and the shape volume of the at least one object to generate a three-dimensional model (). An apparatus for generating a 3D model, and a computer readable medium for generating the 3D model.

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County Dublin, Ireland

David S Monaghan