Abstract: A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

Abstract: A method is provided for categorizing a problem report in a repository of a problem reporting system as an enhancement request or as a defect. Based on the problem report being the defect and having a high severity level, determining a score indicating a probability that a text portion of the problem report includes problem reproduction steps. In response to the score indicating the probability of problem reproduction steps, flagging the problem report to automate. For each problem report to automate, copying the text portion of the problem report to an automated test script, whereby the test script runs in a continuous pipeline.

Abstract: A classifier receives a document from a multi-document transaction. The classifier analyzes the document to identify one or more embedded dates in the content of the document and context of one or more positions of the one or more embedded dates in the document. The classifier evaluates each of the one or more embedded dates based on the separate context of each of the one or more positions within the document and a relative age of the one or more embedded dates in view of temporal characteristics of multiple categories of documents of a transaction to select a particular category associated with the document from among the multiple categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among multiple logical types.

Abstract: A method, computer program product, and computer system for analyzing one or more existing book indexes to build a statistical model of term-to-text pairs. A document may be analyzed, wherein the document may include at least a portion of an instruction in a book. A term in the document may be identified. Whether the term is a candidate for an index of the book may be identified based upon, at least in part, the statistical model of term-to-text pairs.

Abstract: A classifier receives a document and analyzes the document to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.

Abstract: Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Abstract: An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

Abstract: Mechanisms are provided for automated medical device regulation risk analysis (MDRRA). An automated MDRRA engine receives compliance rules for a medical device, a data dictionary for a patient data source, and medical device characteristics data for the medical device. The automated MDRRA engine automatically determines, based on the compliance rules and the data dictionary, critical fields of patient data and medical device characteristics data for demonstrating medical device efficacy according to the compliance rules. The automated MDRRA engine automatically determines, based on the critical fields, one or more patient population data subsets for a plurality of patients from a patient population data set and selects a population segment from the one or more patient population data sets to populate an electronic case report form (eCRF) study build. The automated MDRRA engine automatically populates fields of the eCRF with data from the population data sets corresponding to the selected population segment.

Abstract: Mechanisms are provided for training and operating a Question and Answer (QA) system pipeline. A corpus of information is received which comprises historical data to which one or more filter criteria are applied to extract filtered historical data relevant to a training objective for training the QA system pipeline. Attribute data, action data, and temporal characteristic data are captured from the filtered historical data. An answer key entry is automatically generated in an automatically generated training answer key data structure based on the attribute data, action data, and temporal characteristic data. The correct answer associated with the answer key entry is an action specified by the action data. The temporal characteristic data provides a historical context for the answer key entry. The QA system pipeline is trained using the automatically generated training answer key data structure.

Abstract: Mechanisms are provided which configure a medical treatment recommendation system to implement a medical condition independent treatment recommendation model. The medical condition independent treatment recommendation model operates on medical condition independent scoring features that are independent of any specific medical condition. The mechanisms configure the medical treatment recommendation system to receive a medical condition cartridge providing at least one medical condition specific evaluation feature that is specific to a medical condition, and process patient information based on a combination of an application of the at least one medical condition specific evaluation feature, and an execution of the medical condition independent treatment recommendation model of the medical treatment recommendation system, to generate a treatment recommendation for a patient medical condition associated with the patient. The mechanisms then output the treatment recommendation for the patient.

Abstract: Based on natural language processing of a passage of a first electronic document, a determination is made that the passage indicates an action to be undertaken. In response to this determination and based on an analysis of a second electronic document, the second electronic document is identified as incorporating content preconfigured to allow completion of the action. In response to this identification, the passage is linked to the second electronic document.

Abstract: An approach is disclosed for moving personal and sensitive data from a source filesystem to a destination filesystem while enforcing a source privacy legal framework. A request to copy information from a file residing in the source filesystem enabled to enforce the privacy and control legal framework to a destination filesystem is received. Metadata associated with the file and the request is analyzed to determine a copying policy. The copying policy is applied to the contents of the file to ensure compliance with the privacy and control legal framework of the source filesystem.

Abstract: An approach is disclosed for placing data to manage access based on access and data sensitivity on volumes, An infrastructure is provided to separate data according to a data sensitivity and a data usage, wherein a highly accessed data is separated from lighted accessed data, The infrastructure facilitates efficiency of access by automatically adjusting placement of data and adjusting encryption policies based on a type of data, a sensitivity of the data, and an access activity to the data. The infrastructure enforces the access to the separated data.

Abstract: An approach is disclosed for building a study cohort by collecting information related to a plurality of people according to a collection request from a user. An authority of the request from the user is validated and if validated, a file policy associated with each file containing personal data is checked to verify a type of processing consented by the corresponding individual for their data. The data is transformed, and information copied related to the plurality of people according to the collection request, the request assessment, the consent information as part of each file's metadata, and the privacy legal framework. The privacy legal framework may be based on source files wherein each source file has a source file consent permission to form copied content. The copied content is used to process the collection request.

Abstract: An approach is disclosed that enforces a privacy legal framework filesystem along with an operating system (OS) to enforce the privacy legal framework. An access of a datum in a selected file in the filesystem includes accessing a metadata associated with the selected file where the metadata includes a privacy state and an owner consent-based access policy. The consent-based access policy is enforced by the OS.

Abstract: An approach is disclosed for providing built-in consent permissions for users, groups, processes, and programs accessing a part of a filesystem. The method includes integrating a consent access control into a plurality of files in a filesystem. A first request from a first requestor to access information from a first file in the filesystem is received. A first access policy for the first requestor is determined. The first access policy includes a first selective data conversion. A second request from a second requestor to access information from the first file in the filesystem is received. A second access policy for the second requestor different from the first access policy for the first requestor is determined.

Abstract: An approach is disclosed that enforces restrictions to data in a filesystem based on metadata for a file including a name for an attribute, a type, and a location in the file for the type. The restrictions for file access may be placed directly into a file handler, which is driven by the file structure metadata which identifies types of information, where in the file each type of information is located, and consent information which specifies what type of information is accessible to a requestor retrieving data for a specific purpose.

Abstract: Embodiments relate to an intelligent computer platform to support natural language (NL) processing. The request is analyzed and a lexical answer type (LAT) related to the received request is identified. A knowledge graph (KG) related to the LAT is identified and leveraged to extract a first concept related to the LAT and a second concept related to the first concept. First and second clusters are created, with the first cluster having the LAT and first concept as qualifiers, and the second cluster having the first and second concepts as qualifiers. Each of the formed clusters is populated with one or more documents. An inter-cluster assessment is conducted based on the relevancy of the populated document(s) to the received input. In addition, a machine learning model (MLM) corresponding to the KG is identified and utilized to selectively augment the MLM with the LAT, first and second concepts, and a corresponding relationship to the inter-cluster assessment.

Abstract: A method of machine learning that teaches a computer to determine likelihood that a medical journal article is classified as high value for an intended system. In some embodiments, the method includes procuring, in a medical articles database on the computer, a training set including medical articles recommended by subject matter experts associated with medical journals that have published the medical articles. The method can also include identifying, by a feature extraction controller of the computer, first features in the medical articles via a remote annotation service and a remote article information service. The method can also include identifying, by intended system services electronically available to the intended system, second features in the medical articles. The method can also include reducing, by the feature extraction controller, the first and second features to form a set of relevant features.

Abstract: Embodiments relate to an intelligent computer platform to identify a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept. The LAT, together with the first and second concepts are utilized to create a first and second cluster. Documents are selectively populated into the clusters. The clusters are subject to sorting based on a relevancy protocol.