Abstract: Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity sc

Abstract: Techniques for workforce management that include: vectorizing documents associated with workers to generate vector representations of the documents; generating, based on the vector representations of the documents, worker vector sets; generating, based on the worker vector sets, labeled worker datasets comprising position labels; clustering, based on the position labels, the labeled worker datasets to determine position clusters; determining, based on the labeled worker datasets, an attribute vector set for a given position; obtaining candidate documents associated with a candidate worker; vectorizing the candidate documents to generate vector representations of the candidate documents; generating, based on the vector representations of the documents, a candidate worker vector set; comparing the candidate worker vector set to the attribute vector set for the given position to determine whether the candidate worker associated with the candidate worker vector set is qualified for the given position.

Abstract: Product distribution including receiving external conditions data indicative of environmental conditions concerning distribution of products from a point of sale (POS), determining a POS product distribution model for the POS, including first and second product distribution models operable to determine distribution of the first and second products to the POS based on external conditions data, determining, based on application of the external conditions data to the POS product distribution model, a product distribution to the POS, including application of the external conditions data to the first and second product distribution model to determine distributions of the first and second products to the POS, and providing supply instructions for the POS, including first and second product supply instructions to cause the product supply network to provide the distribution of the first and second product to the POS.

Abstract: A method includes receiving historical data housed in the one or more computer systems, the historical data including structured data and unstructured data, storing the historical data in a central database, aggregating the historical data in the central database according to subject matter, validating the aggregated historical data, analyzing the validated aggregated historical data using a series of tools, generating normal profiles in the first computer system from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data housed in the one or more computer systems, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based, predictive and interventive outcomes for out of normal profiles based on parameters for investigation.

Abstract: Techniques for cycle management including: determining, based on historical unstructured CRM data, a document model to determine document attributes of a set of documents; determining, based on historical structured CRM data, a stage prediction model to determine attributes of a current sales cycle based on a current sales cycle stage and current document attributes; determining, based on current sales cycle structured data, a current stage of the current sales cycle; determining, based on application of current sales cycle unstructured data to the document model, a current set of attributes for the current sales cycle; and determining, based on application of the current stage of the current sales cycle and the current set of attributes for the current sales cycle to the stage prediction model, a predicted outcome for the current sales cycle.

Abstract: Techniques for item transactions including generating, based on structured transaction data indicative of values of an item over a time interval, a historical structured transaction dataset corresponding to the time interval, determining, based on the historical structured dataset, a set of historical directional values for the time interval; generating, based on unstructured transaction data comprising datasets published over the time interval, a historical unstructured transaction dataset corresponding to the time interval, determining numerical representations of the datasets; determining, based on the set of historical directional values and the numerical representations, a transaction model to determine a predicted direction of value for the item based on current structured and unstructured transaction data; determining, based on application of current structured and unstructured transaction data to the transaction model, a predicted direction of value for the item.

Abstract: Product distribution including receiving external conditions data indicative of environmental conditions concerning distribution of products from a point of sale (POS), determining a POS product distribution model for the POS, including first and second product distribution models operable to determine distribution of the first and second products to the POS based on external conditions data, determining, based on application of the external conditions data to the POS product distribution model, a product distribution to the POS, including application of the external conditions data to the first and second product distribution model to determine distributions of the first and second products to the POS, and providing supply instructions for the POS, including first and second product supply instructions to cause the product supply network to provide the distribution of the first and second product to the POS.

Abstract: Techniques for managing work tickets, including receiving a work ticket including a textual description of a technical issue to be resolved, determining, based on the textual description, ticket data including an issue description indicative of the technical issue to be resolved, determining, based on application of the issue description to a group mapping model, an agent group corresponding to the technical issue, determining, based on application of the agent group to an agent mapping model, an agent of the group for resolving the technical issue, and providing, in response to determining the agent of the group for resolving the technical issue, the work ticket to the agent.

Abstract: Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity sc

Abstract: A method includes receiving historical data housed in the one or more computer systems, the historical data including structured data and unstructured data, storing the historical data in a central database, aggregating the historical data in the central database according to subject matter, validating the aggregated historical data, analyzing the validated aggregated historical data using a series of tools, generating normal profiles in the first computer system from the analyzed validated aggregated historical data, storing the generated normal profiles as horizon data sets, receiving real time data housed in the one or more computer systems, comparing the real time data to the horizon data sets to identify normal and out of normal profiles, and generating rules-based, predictive and interventive outcomes for out of normal profiles based on parameters for investigation.