Abstract: Embodiments for providing clinical practice guidelines by a processor. One or more clinical practice guidelines (CPGs) may be automatically generated according for one or more cohorts using evidence data and patient data from one or more data sources.

Abstract: A system and method for analyzing and explaining the temporal evolution of policies. The analysis of the temporal evolution of policies includes determining semantically related changes in the text of the corresponding policies over time and across the different versions of the (two or more) policy documents. An explanation of the temporal evolution of policies is provided consisting of human interpretable information relating each change to one or more events and/or contextual data. A next steps policy change is also suggested that includes a set of policy conditions based on potential future changes of similar policies and contextual data and event data. The suggestions are related to the policy relevant to the target cohort or individual. The system adapts machine learned models by receiving user feedback provided on an analysis of the correctness of the temporal evolution of policies, corresponding explanations and suggestions of the next actions.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate contextual comparison of semantics in conditions of different policies are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a comparison component that contextually compares semantics of conditions in policy data of different policies based on a feature of at least one entity. The computer executable components further comprise a contextualization component that employs a model to provide a contextual explanation of how a first condition in first policy data of a first policy is semantically different from a second condition in second policy data of a second policy based on the feature of the at least one entity.

Abstract: Embodiments are provided for providing predictive computing and data analytics for project management in a computing system by a processor. A lifecycle of each of a plurality of objects may be monitored based on data received from a plurality of data sources. Predictive analytics for project management of each of the plurality of objects may be provide based on monitoring the lifecycle of each of the plurality of objects.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically updated with one or more changes to a codebase.

Abstract: A first set of an entity is received, where the first set of data includes distinct characteristics of the entity. A second set of data on one or more domains of the entity is received. Using the first and second set of data, a probabilistic knowledge graph for the entity is generated that includes an entity node, a first plurality of nodes, and a second plurality of nodes. The first plurality of nodes are connected to the entity node and represent each of the distinct characteristics. The second plurality of nodes are connected via probabilistic edges, where each of these probabilistic edges has an associated confidence score. This confidence score is determined using the second set of data.

Abstract: Embodiments for providing an enhanced codebase in a computing environment by a processor. One or more container specification files may be automatically generated for a codebase based on one or more extracted attribute names and values.

Abstract: Embodiments for intelligent collaborative generation or enhancement of useful medical actions to a user by a processor. One or more useful medical actions may be recommended, with evidence in support thereof, for positively impacting a health state of a user according to data relating to similar users. One or more of the recommended user medical actions may be matched to one or more selected portions of clinical practice guidelines (CPGs). The one or more of the recommended user medical actions may be added as an additional CPG or as an enhancement to one or more of the matching portions of the recommended user medical actions.

Abstract: Various embodiments are provided for correlating regulatory data in a computing environment by a processor. A rule may be associated with one or more textual paragraphs extracted from a policy document that describes at least a portion of the rule.

Abstract: Embodiments for implementing intelligent ranking of sections of clinical practice guidelines by a processor. One or more clinical practice guidelines (CPGs) may be identified and analyzed. A score may be assigned to one or more sections of the CPGs according evidential data identified in one or more data sources. The one or more sections of the CPGs may be ranked according to the scoring.

Abstract: In an approach for predicting an effectiveness of a given social program for a given patient at one or more points in time and for providing a score indicating the accuracy of the prediction and an explanation of the prediction, a processor receives a request from a user. Responsive to determining a social program database contains historical data on the given social program, a processor analyzes a set of patients associated with the given social program. A processor predicts the effectiveness of the given social program for the given patient at the one or more points in time using a prediction model trained to predict an effectiveness score and a confidence score. A processor outputs a prediction of the effectiveness of the given social program for the given patient at the one or more points in time to the user.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate contextual comparison of semantics in conditions of different policies are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a comparison component that contextually compares semantics of conditions in policy data of different policies based on a feature of at least one entity. The computer executable components further comprise a contextualization component that employs a model to provide a contextual explanation of how a first condition in first policy data of a first policy is semantically different from a second condition in second policy data of a second policy based on the feature of the at least one entity.

Abstract: A neural network is trained with patient historical data including features of patient pathways and labels characterizing a type of the patient pathways. During a runtime phase, the trained neural network matches a current patient profile with the patient historical data to extract selected portions of clinical practice guidelines (CPGs) identified according to one or more generated CPG models, the patient pathway models, and the current patient profile.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate contextual comparison of semantics in conditions of different policies are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a comparison component that contextually compares semantics of conditions in policy data of different policies based on a feature of at least one entity. The computer executable components further comprise a contextualization component that employs a model to provide a contextual explanation of how a first condition in first policy data of a first policy is semantically different from a second condition in second policy data of a second policy based on the feature of the at least one entity.

Abstract: Embodiments for intelligent generation of customized questions or questionnaires by a processor. One or more customized questions or questionnaires may be generated according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a domain experts, domain knowledge, historical questions or questionnaires, or a combination thereof using a machine learning operation.

Abstract: In an approach for explaining a theorem proving model, a processor predicts a truth value of a query through a pre-trained theorem proving model, based on the query and one or more facts and rules in a knowledge base. A processor ranks the one or more facts and rules according to contribution, calculated in a pre-defined scoring method, made to the predicted truth value of the query. A processor generates a proof of the predicted truth value, wherein the proof is one or more logical steps that demonstrate the predicted truth value in a natural language. A processor outputs the proof.

Abstract: A method, system and apparatus for identifying and explaining events, including determining at least one event from a plurality of events, determining at least one outcome defined by an outcome plan, and identifying a relationship between the at least one event and the at least one outcome.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining a textual knowledge base; filtering the textual knowledge base to obtain a subset of the textual knowledge base, wherein the filtering is based on textual query data; generating reasoning data based on the subset of the textual knowledge base and the textual query data; generating classification data based on the subset of the textual knowledge base, the textual query data, and the reasoning data; and providing label data as output for the textual query data based on the classification data.

Abstract: Embodiments for implementing intelligent recommendations of convenient event opportunities by a processor. A group of entities may be identified for one or more event opportunities or the one or more event opportunities may be identified for the group of entities according to one or more entity selection criteria and one or more event criteria. The one or more event opportunities and the group of entities may be matched according to a level of convenience for attending the one or more event opportunities of the group of entities. The one or more matching event opportunities may be ranked and suggested to the group of entities.