Abstract: Embodiments for cause identification in audit data by a processor. A probabilistic logical representation is extracted from text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain. A root cause and one or more causal factors of the one or more reoccurring problems is automatically identified using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors.

Abstract: Implementations are directed to computer-implemented, real-time benefit eligibility evaluation including actions of receiving a request in a computer-readable format, the request indicating a procedure, a policy, and providing metadata, and determining that a sufficiently similar request has not been previously received, and at least partially in response: providing a set of policies, determining similarity scores between the policy, and each policy in the set of policies to provide a set of similarity scores, identifying a most similar policy in the set of policies based on similarity scores in the set of similarity scores, and outputting a decision to the request, the decision including a historical decision previously output for the most similar policy.

Abstract: This document describes systems, methods, devices, and other techniques for automatically identifying and extracting medical conditions and supporting evidences from electronic health records. In some implementations, formatted text extracted from an unstructured electronic health record is obtained. The formatted text is segmented into multiple documents, wherein each document comprises a respective document type and represents a respective document encounter. Medical condition entities and supporting evidence entities referenced in each of the multiple documents are extracted. Extracted supporting evidence entities within a same document are linked to respective extracted medical condition entities from the same document using one or more of i) medical ontologies, or ii) a medical knowledge base. Output data representing linked supporting evidence entities and medical condition entities within a same document is provided.

Abstract: Embodiments for cause identification in audit data by a processor. A probabilistic logical representation is extracted from text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain. A root cause and one or more causal factors of the one or more reoccurring problems is automatically identified using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors.

Abstract: An approach to automatically generating a survey. The approach collects input information from a survey designer and uses the input to generate sections for the survey. The approach then selects question from related archived surveys and generates new questions from internal and/or external data sources to assemble the survey. The approach can perform the section generation analysis and the question selection/generation automatically and/or tuned by a survey designer. The approach distributes the completed survey to survey takers and feedback is provided based on metrics associated with characteristics exhibited by the survey takers while taking the survey. The feedback allows the approach to improve survey quality.

Abstract: A system and computer program product that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the system identifies one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The system further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.

Abstract: A system and method that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the method includes identifying one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The method further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.

Abstract: A system and method that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the method includes identifying one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The method further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.

Abstract: A system and computer program product that facilitates authoring of a Bayesian Belief Networks by: accessing text content stored in a content storage device; identifying statements within said accessed text content indicating a dependence relation; extracting said statements indicating said dependence relation from said text content; and aggregating said extracted statements into a form suitable for representation as a BBN network structure. To identify statements indicating a dependence relation, the system identifies one or more lexical and semantic attributes of variables within a text unit indicating a conditional dependence relation between two or more variables. The system further processes the text content to extract probabilistic information and probability statements and aggregate the probability statements into a quantitative layer of the BBN structure.