International Business Machines Corporation

Joao H Bettencourt-Silva

Marco Luca Sbodio

Natalia Mulligan

Vanessa Lopez Garcia

Theodora Brisimi

Elizabeth Daly

Yassine Lassoued

Giovanni Russo

Julien Monteil

Martin Stephenson

Alessandra Pascale

Valentina Rho

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.

Providing clinical practical guidelines

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.

Analyzing and explaining a temporal evolution of policies and suggesting next steps

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.

Contextual comparison of semantics in conditions of different policies

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.

Intelligent collaborative generation or enhancement of useful medical actions

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.

Intelligent ranking of sections of clinical practical guidelines

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.

Intelligent identification of appropriate sections of clinical practical guideline

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.

Intelligent recommendation of convenient event opportunities

Embodiments for implementing an intelligent health recommendation service by a processor. A health state of a user may be learned according to user behavior, physical conditions of the user, activities of daily living (ADL) and associated context of daily living (CDL) of the user, or a combination thereof. One or more applications, one or more application components, one or more internet of things (IoT) devices, or a combination thereof may be identified and recommended to minimize one or more possible negative impacts upon the health state of the user.

Intelligent health recommendation service

Embodiments for intelligent interpretation of image processing results using machine learning in a computing environment by a processor. One or more data sets may be transformed into one or more pseudo-image representations to enable one or more image processing tasks for image processing. An interpretation of an image processing task result from applying the one or more image processing tasks on the one or more pseudo-image representations generated from one or more data sets.

Intelligent generation of image-like representations of ordered and heterogenous data to enable explainability of artificial intelligence results

Embodiments for intelligent learning for explaining anomalies to a user by a processor. One or more anomalous records may be identified in a knowledge base. A list of ranked candidate explanations may be generated for the one or more anomalous records. An active learning dialog may be initiated with one or more users to increase accuracy of the knowledge base, a domain knowledge, and each of the ranked candidate explanations.

Intelligent learning for explaining anomalies

Embodiments for intelligent recommendation of useful medical actions to a user by a processor. One or more useful medical actions may be cognitively recommended with evidence in support thereof for impacting a health state of a user according to historical data collected from one or more data sources, one or more user profiles, a domain knowledge, feedback data, or a combination thereof. The one or more useful medical actions may be ranked according to a scoring criteria, the domain knowledge, the historical data, a set of rules, previously recommended medical actions, or a combination thereof.

Intelligent recommendation of useful medical actions

Embodiments for intelligent facilitation of communication by a processor. A communication error in one or more selected portions of a communication of a user may be identified. The communication error in the one or more selected portions of the communication may be automatically translated.

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Dublin, Ireland

Joao H Bettencourt-Silva