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.

Abstract: Systems and methods are disclosed for determining unnecessary internal system utilization based on protocol adherence. A method includes receiving a first data object, generating an entity data object, and generating a verified entity data object based on comparing one or more metrics of the entity data object against one or more predetermined threshold values, wherein entities of the verified entity data object are a subset of the entities of the entity data object. The method further includes generating a compliance indicator for each entity of the verified entity data object. The method furthermore includes generating a utilization adjustment data object and causing the utilization adjustment data object to be displayed on a Graphical User Interface (GUI).

Abstract: are disclosed. A pill identification request, including one or more images of a pill and a user identifier of a user associated with the pill, is received. A first machine learning system is used to generate one or more image embeddings based on the one or more images. The user identifier is used to retrieve claims data of the user, and the claims data are encoded to generate a claims embedding. A second machine learning system is used to identify the pill based on the one or more image embeddings and the claims embedding. A response to the pill identification request is generated based on the identifying.

Abstract: Systems and methods are disclosed for scheduling appointments based on changing health conditions of users. The method includes inputting the initial health dataset of a user to a machine learning model configured to identify other users with similar health profiles. A frequency of monitoring, a frequency of appointments, a duration between the appointments, or a type and length of the appointments is determined to generate a schedule of appointments. A subsequent health dataset of the user is received, and health scores, rule scores, or medication scores for the user are determined based on the subsequent health dataset. A plurality of risk scores for the user is evaluated based on the health scores, rule scores, or medication scores. A recent risk score is determined based on a change in the plurality of risk scores. The schedule of appointments is adjusted based on the recent risk score and the user is notified.

Abstract: Systems, methods, and apparatuses implementing a display optimization system are provided herein. In some embodiments, an example display optimization system may be configured to perform an optimal anchor-prior matching operation to identify optimal anchor-prior image pairs or series pairs from new medical imaging data (e.g., one or more anchor image series) and historical medical imaging data (e.g., one or more prior image series).

Abstract: Various embodiments of the present invention address technical challenges associated with performing machine learning operations on timeseries/periodic data by introducing a machine learning framework that has a first periodic tier for determining predicted evaluation scores for those predictive entities that are associated with a single evaluation period (e.g., a single year of data) and a second periodic tier for determining predicted evaluation scores for those predictive entities that are associated with multiple evaluation periods. The noted framework addresses the existing shortcomings of machine learning frameworks that operate on timeseries/periodic data with respect to inadequacy of data associated with shorter periods to determine parameters needed to perform comprehensive predictive data analysis with respect to longer periods.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying one or more evidence text portions comprising one or more bases relied on by a generative machine learning model for assigning a plurality of model-assigned categorical identifiers to a plurality of text segment data objects associated with a document data object, and verifying the one or more evidence text portions with a verifier machine learning model to generate one or more classifications of the document data object and provide the one or more verified evidence text portions along with the one or more classifications.