Abstract: Embodiments utilize interpolation to smooth animation frames by generating synthetic Shapley values utilized to generate the animation. Additionally, some embodiments provide for generation of an embedding space for improved understandability of the model training based on corresponding Shapley values. The embedding space is mapped to using activations derived from a specially configured LSTM for each model, where an embedded representation of the LSTM activations is generated by a specially configured autoencoder such that the embedded representation may be mapped to the embedding space. The embedding space and animations are renderable to an interface for user investigation and may be further customizable to narrow investigation on particular data thereof.

Abstract: Various embodiments of the present disclosure provide machine learning training techniques for implementing a multi-modal interpretation process to generate holistic outputs for an event. The techniques may include generating, using first layers of a multi-modal machine learning model, text-based intermediate representations for an entity based on textual input data. The techniques include generating, using second layers of the multi-modal machine learning model, image-based intermediate representations for the entity based on the text-based intermediate representations and input images for the entity. The techniques include generating, using one or more third layers of the multi-modal machine learning model, an entity representation summary based on the image-based intermediate representations and an image narrative summary for the input images. The techniques include initiating the performance of a prediction-based action based on the entity representation summary.

Abstract: Various embodiments of the present invention disclose techniques for traversal path optimization given a traversal network comprising a group of nodes comprising a plurality of navigation orchestration nodes and using a traversal path optimization machine learning model. In some embodiments, a path feature set is determined for each candidate traversal path of a plurality of candidate traversal paths. A traversal path optimization machine learning model is configured to generate path scores for each candidate traversal path based at least in part on the path feature set.

Abstract: Techniques for data clustering and outcome generation may comprise determining a state sequence associated with a sample of one or more samples that satisfies an occurrence threshold and generating one or more timing features for the state sequence. The techniques may further comprise clustering, by executing a machine-learned model, the sample corresponding to at least the state sequence into a cluster of a set of clusters based on the state sequence and the one or more timing features. The techniques may further comprise generating a data object indicating one or more outcomes associated with the cluster that includes the sample. These data objects indicate outcomes that are specific enough to provide meaningful insights contained in associations between the input data and the associated clusters and eliminate/reduce the misleading information commonly output by typical techniques.

Abstract: Techniques for automatically detecting inactive addresses of providers using unsupervised learning approaches are provided. The techniques include determining an activity trend of an address. Responsive to determining that the address is associated with increasing activity, determining an active metric of the address, which indicates a likelihood of the address being active. Responsive to determining that the address is associated with decreasing activity, determining an inactive metric of the address, which indicates a likelihood of the address being inactive. The techniques further include determining whether the address is active or inactive based on the active metric and/or inactive metric. In some embodiments, the active metric or inactive metric is a weighted sum of z scores determined based on Gaussian distributions generated with respect to various benchmarks or provider features.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing suggestion keywords based on historical search data of a user by: generating one or more keyword feature vectors associated with a plurality of keywords from a list of suggestion keywords, generating one or more personalized feature vectors associated with the user based on search session data, generating a plurality of predictions of the user selecting the plurality of keywords based on the one or more keyword feature vectors and the one or more personalized feature vectors, assigning a plurality of rankings to the plurality of keywords based on the plurality of prediction probabilities, and generating one or more typeahead suggestion keywords based on the plurality of rankings.

Abstract: A method includes receiving, by one or more processors, a dataset including transition data and factor data. The method includes generating a feature for a machine learning model based on the transition data, generating, via input of at least the feature into the machine learning model, one or more data objects indicative of a transition prediction for a transition from the first stage to the second stage, the machine learning model having been trained: with data sources including training factor data having information other than a chemical constituent of blood, and to output information associated with a transition prediction. The method further includes initiating performance of one or more remedial or analytical actions in response to generating the one or more data objects indicative of the transition prediction.

Abstract: Disclosed are systems and methods for detecting a table from a document, classifying each cell of the table as one of: a value cell, a row header cell, or a column header cell, and performing a bounding box elongation operation to match each cell that is classified as a value cell to a first corresponding cell that is identified as a row header cell and a second corresponding cell that is identified as a column header cell. For each cell classified as a value cell, a data tuple is generated comprising a row header element, a column header element, and a value element, wherein the row header element corresponds to a first value in the first corresponding cell, the column header element corresponds to a second value in the second corresponding cell, and the value element corresponds to a third value in the value cell.

Abstract: A method includes performing by a host system processor: providing a database including a plurality of records, the database having at least one attribute associated therewith; determining a plurality of maximum block sizes as a plurality of ideal maximum numbers of the plurality of records in a plurality of blocks, respectively, based on a plurality of blocking keys; determining a duplication factor based on a number of unique records of the plurality of records based on all of the at least one attribute; and generating candidate pairs of the plurality of records for linkage based on the plurality of maximum block sizes and the duplication factor.

Abstract: A method includes receiving, by one or more processors, a clinical image associated with a dental procedure; identifying, by the one or more processors, one or more dental procedure codes based on processing the clinical image using a plurality of AI models; receiving, by the one or more processors, a periodontal chart image; processing, by the one or more processors, 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; identifying, by the one or more processors, a submitted dental procedure code in a dental claim for the dental procedure; determining, by the one or more processors, whether the submitted dental procedure code corresponds to a visibly detectable procedure; determining, by the one or more processors, whether the submitted dental procedure code matches any of the one or more dental procedure codes based on processing the clinical image when the submi

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.