Abstract: As described herein, various embodiments of the present invention use an output space refinement machine learning model to filter C of the B candidate predictive associations that are referred to herein as dynamically-preselected candidate predictive associations for each prediction input data object, with every prediction input data object being associated with a different subset of the dynamically-preselected candidate predictive associations, where C is less than B and is in some embodiments typically much less than B. As described in greater detail below, this approach reduces the number of computational operations that need to be performed by a final classification machine learning model (referred to herein as a variable-output-space prediction machine learning model), and leads to substantial computational efficiency advantages relative to naïve implementations.

Abstract: Various embodiments of the present disclosure provide machine learning configuration techniques for seamlessly leveraging compute functionalities from across a plurality of disparate third-party computing resources. The configuration techniques include receiving a first-party workspace request that identifies a third-party computing resource and in response to the first-party workspace request: generating a compute agnostic project workspace hosted by a first-party computing resource, initiating the generation of a third-party workspace hosted by the third-party computing resource, and initiating the configuration of a first-party routine set within the third-party workspace. The first-party routine set includes a plurality of webhooks that facilitate communication between the first-party computing resource and the third-party computing resource, thereby enabling a first-party to leverage multiple different, traditionally incompatible, computing functionalities from one centralized location.

Abstract: A method includes receiving a periodontal chart image; processing the periodontal chart image using optical character recognition to obtain pocket measurements associated with a plurality of teeth along with positional coordinates of each of the pocket measurements; applying layout rules associated with the periodontal chart image to identify which respective ones of the pocket measurements correspond to which respective ones of the plurality of teeth; and storing the pocket measurements in a computer-readable construct having a specified data format that preserves relationships between the respective ones of the pocket measurements and the respective one of the plurality of teeth.

Abstract: Embodiments herein relate to viral transfer risk management. In example embodiments, an apparatus is configured to retrieve a first user identifier associated with a first client computing entity. The apparatus is further configured to determine, based at least in part on a first transfer risk score associated with the first user identifier, a first transfer risk score grouping for the first user identifier. The apparatus is further configured to, based at least in part on the first transfer risk score grouping and physical space parameters associated with a physical space identifier, allocate a first physical space assignment within a physical space associated with the physical space identifier to the first user identifier associated with the first client computing entity.

Abstract: Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques may include generating an intake data object including an intake identifier and an object identifier respectively corresponding to a plurality of intake objects. The intake data object is used to generate transitioning data objects corresponding to a subset of the intake objects. The intake data object may be modified with a transition identifier to link the objects. In addition, one or both identifiers may be added to a backstop data object to link the subset of intake objects to a backstop location. In response to a request, the subset of intake objects may be moved from the backstop location to conveyor pallet, and, in response, the transition and intake identifier may be removed from the backstop data object and added to a conveyor pallet data object.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for i) initializing a global model with an initial probability distribution that predicts the likelihood of a target classification, and ii) continuously learning from local data sets across a plurality of edge computing entities over time by: a) generating local machine learning models based on the global machine learning model at edge computing entities, b) observing datasets via the local machine learning models, c) aggregating learnings from the local machine learning models, d) updating the global machine learning model to reflect the aggregated learnings, and e) cascading the updated model to propagate the aggregated learnings across the edge computing entities.

Abstract: Systems and methods are disclosed for generating a personalized care path for a patient. The method includes receiving, by one or more processors, relevant data associated with the patient from a plurality of data sources. The relevant data includes demographic data and medical data associated with the patient. The one or more processors using a graph convolutional neural network-based model determine the personalized care path for the patient based on the relevant data associated with the patient. The graph convolutional neural network-based model is trained based on a plurality of care paths of a plurality of patients represented by a patient-bucket-procedure (PBP) graph. The one or more processors provide data associated with the determined personalized care path for the patient to a device associated with a user.

Abstract: A method includes receiving input information associated with a health record of a patient, the input information comprising a plurality of input variable tokens; embedding the plurality of input variable tokens to generate a plurality of input variable token vectors, respectively; aggregating the plurality of input variable token vectors to generate a patient health record vector; generating, using an artificial intelligence model, an identification of a criterion used for determining an appropriateness of a care plan for the patient based on the patient health record vector; and generating a ranking of respective ones of the plurality of input variable tokens based on how much each of the plurality of input variable tokens contributed to the identification of the criterion.

Abstract: Systems and computer-implemented methods are disclosed for detecting a system anomaly. A computer-implemented method comprises: receiving, by a data storage module, time-series data from a plurality of sensors of an information technology infrastructure, each sensor corresponding to a respective metric; detecting a plurality of anomalies in the time-series data stored in the data storage module; generating a knowledge graph by: determining connections between the plurality of metrics based on the time-series data; and for each connection, determining a respective weight based on an impact score for metrics joined by the connection; and configuring a root cause determination engine to output one or more metrics as root cause candidates, the one or more metrics based on the knowledge graph, in response to input of a query associated with at least one metric.

Abstract: Techniques for standardized interaction classification across multiple communication channels may comprise receiving interaction data associated with a user interaction with a device associated with a communication channel and generating an interaction summary of the user interaction. The techniques may further comprise generating a feature vector of the interaction summary and determining semantic similarity values of the interaction summary feature vector and one or more feature vectors representing interaction description taxonomies of a standardized interaction classification schema. The techniques may further comprise determining an interaction label of the schema that corresponds to the interaction summary and generating a data object that indicates the interaction label. These techniques generate accurate, generalizable interaction labels without performing significantly redundant computing processes and/or otherwise occupying substantial computing resources.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that risk score generation predictive data analysis by utilizing at least one of inferred hybrid risk score generation machine learning models and hybrid graph-based machine learning models.

Abstract: Hierarchical data objects are generated via a computer-based system for applying a series of rules to establish episode-specific data objects reflecting a plurality of discrete claim records before further dissecting the generated episode-specific data objects prior to finalization of those episode-specific data objects to identify claim records within the episode-specific data objects that are eligible for generation of one or more sub-episodes within the episode-specific data objects. The identified sub-episodes are reflected within the episode-specific data object to designate complete episodes of care that additionally reflect interactions with the corresponding parent episode.

Abstract: There is a need for solutions for more effective and efficient predictive data analysis systems in conceptually hierarchical domains. This need can be addressed, for example, by a system configured to obtain one or more initial raw inputs; determine a partial prediction for the one or more initial raw inputs, wherein the partial prediction is associated with an initial encoding hierarchy and the initial encoding hierarchy is associated with a plurality of prediction nodes; determine, based on the partial prediction and the initial encoding hierarchy, one or more partial prediction information deficiencies for partial prediction; obtain one or more supplemental raw inputs based on the one or more partial prediction information deficiencies; and generate a conceptually hierarchical prediction based on the one or more supplemental raw inputs and the partial prediction.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating predicted recommendations by using an input entity representation, a reference entity representation, and collaborative filtering machine learning model.

Abstract: Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques of the present disclosure may include receiving a transition identifier corresponding to a transitioning data object that include (a) a first object count of a plurality of transitioning objects within a transitioning container, (b) an intake identifier corresponding to the plurality of transitioning objects, and (c) a pallet identifier for a conveyor pallet configured to move the transitioning container.

Abstract: A computer-implemented method for identifying a problem list section from an electronic document includes receiving, by one or more processors, the electronic document, generating, by the one or more processors and based on applying an optical character recognition algorithm to the electronic document, unstructured text, and identifying, by the one or more processors, one or more problem list words in the unstructured text, the one or more problem list words belonging in a dataset for identifying a presence of a problem list section. The method also includes associating, by the one or more processors, a portion of the unstructured text that corresponds to the one or more problem list words in the unstructured text with the problem list section and outputting, by the one or more processors, at least a portion of the problem list section.

Abstract: A method comprises receiving an incident report comprising a textual description of an incident; generating a regularized incident report in which out-of-vocabulary terms in the received incident report are replaced with in-vocabulary terms; determining importance measures for a plurality of incident report terms, wherein each of the incident report terms is in the regularized incident report; generating an incident matrix in which similarity values are defined for combinations of terms in the incident report and terms in a predetermined term set; generating an incident vector based on the incident matrix and the importance measures for the terms in the incident report; applying one or more machine learning (ML) models that identify, based on the incident vector, relevant software support records and/or software modules, wherein the relevant software support records and the software modules are potentially relevant to the incident; and outputting data identifying relevant software support records and/or softw

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating domain-specific queries that are semantically similar to a search query by spell-correcting and tokenizing a search query, and then generating, using an embeddings dictionary data object associated with one or more domain vocabulary data objects, queries semantically related to the search query based on proximity of one or more similar embeddings to an embedding associated with the tokenized query within a domain vector space.

Abstract: Systems and methods for providing collision-free unique versioned identifiers using a distributed ledger are provided herein. An example system may include a unique identifier provider that is configured to provide evolving, backward compatible, unique versioned identifiers from semi-structured data such as forms or claims to various in a decentralized fashion.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations configured to integrate distinct clustering schemes given temporal variations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating integrative predicted scores based at least in part on at least one of: within-cluster consistency scores determined for clusters determined using a first clustering scheme (e.g., a service clustering scheme), within-cluster consistency scores determined for clusters determined using a second clustering scheme (e.g., a recipient clustering scheme), cross-cluster consistency scores, and cross-temporal consistency scores.