Abstract: Aspects of the present disclosure provide a computer-implemented method that includes obtaining the medical condition of the individual based at least in part on a personal identifier associated with the individual, the medical condition being determined by performing natural language processing of medical information associated with the individual. The method further includes obtaining characteristics of the plurality of items. The method further includes comparing the characteristics of the plurality of items to a restriction associated with the medical condition of the individual. The method further includes, responsive to determining that at least one of the characteristics of the plurality of items violates the restriction associated with the medical condition of the individual, preventing the individual from obtaining the item of the plurality of items associated with the violation.

Abstract: In an approach for enhancing an experience of a user listening to and/or watching an audio-visual content by modifying future audio and/or video frames of the audio-visual content, a processor captures a set of sensor data from an IoT device worn by the first user. A processor analyzes the set of sensor data to generate one or more connotations by converting the emotion using an emotional vector analytics technique and a supervised machine learning technique. A processor scores the one or more connotations on a basis of similarity between the emotion exhibited by the first user and an emotion expected to be provoked by a second user. A processor determines whether a score of the one or more connotations exceeds a pre-configured threshold level. Responsive to determining the score does not exceed the pre-configured threshold level, a processor generates a suggestion for the producer of the audio-visual content.

Abstract: In an approach for enhancing an experience of a user listening to and/or watching an audio-visual content by modifying future audio and/or video frames of the audio-visual content, a processor captures a set of sensor data from an IoT device worn by the first user. A processor analyzes the set of sensor data to generate one or more connotations by converting the emotion using an emotional vector analytics technique and a supervised machine learning technique. A processor scores the one or more connotations on a basis of similarity between the emotion exhibited by the first user and an emotion expected to be provoked by a second user. A processor determines whether a score of the one or more connotations exceeds a pre-configured threshold level. Responsive to determining the score does not exceed the pre-configured threshold level, a processor generates a suggestion for the producer of the audio-visual content.

Abstract: Mechanisms are provided to implement a bias identification engine that identifies bias in the operation of a trained cognitive computing system. A bias risk annotator is configured to identify a plurality of bias triggers in inputs and outputs of the trained cognitive computing system based on a bias risk trigger data structure that specifies terms or phrases that are associated with a bias. An annotated input and an annotated output of the trained cognitive computing system is received and processed by the bias risk annotator to determine if they comprise a portion of content that contains a bias trigger. In response to at least one of the annotated input or annotated output comprising a portion of content containing a bias trigger a notification is transmitted, to an administrator computing device, that specifies the presence of bias in the operation of the trained cognitive computing system.

Abstract: A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category-associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.

Abstract: A mechanism is provided in a data processing system to implement an annotator for annotating content using context-based surface forms. The mechanism receives a dictionary data structure of surface forms comprising a plurality of regular expressions and input content. The mechanism compares a given span of text in the input content to each regular expression in the dictionary data structure. Responsive to the given span of text matching a given regular expression, an annotator annotates the span of text with a content indicator corresponding to a content category associated with the dictionary data structure. The mechanism performs a natural language processing operation on the input content based on results of the annotation.

Abstract: A mechanism is provided to implement a cognitive dictionary builder. The mechanism configures the cognitive dictionary builder with a set of selection criteria comprising a set of rules. The mechanism performs natural language processing on an input document in a corpus of information to analyze a context for each term or phrase in the input document and applies the set of rules to each term or phrase in the input document with respect to its context. The mechanism adds a term or phrase to at least one corresponding dictionary data structure based on a result of applying the set of rules.

Abstract: Techniques for natural language processing based on user context include identifying a context of a user and responsive to receiving a request from the user intended for processing by a natural language processing (NLP) model, accounting for the context of the user in relation to the request. A result from the NLP model having accounted for the context of the user is provided.

Abstract: A computer-implemented method includes receiving, by a natural language processing (NLP) annotator, an input text that is to be annotated. The method further includes determining, by the NLP annotator, a user setting that indicates an aggressiveness level of annotation to be used to annotate the input text. The method further includes selecting, by the NLP annotator, from a plurality of dictionaries, a first dictionary based at least in part on the aggressiveness level. The method further includes generating, by the NLP annotator, annotated text of the input text based at least in part on the first dictionary. The method further includes outputting, by the NLP annotator, the annotated text.

Abstract: A mechanism is provided for re-ranking search results based on a personal medical condition. One or more medical conditions associated with a patient are identified based on an analysis of patient information associated with the patient. The one or more medical conditions are correlated with a set of medical condition content indicator data structures. A search query corresponding to the patient is processed to generate initial search results that are ranked with an initial ranking. The initial search results are analyzed based on a patient specific dictionary data structure corresponding to the one or more medical conditions associated with the patient. The content of the initial search results are re-ranked to generate re-ranked search results having a modified ranking that is based on the one or more medical conditions of the patient. The re-ranked search results are output to the patient in accordance with the modified ranking.

Abstract: A medical condition based content filter mechanism is provided that analyzes patient information associated with a patient to identify medical condition(s) associated with the patient and correlating the medical condition(s) with one or more medical condition content indicator data structures specifying negative content indicators and/or positive content indicators. A user specific content indicator dictionary data structure (USCID) is generated based on the correlation and used to process received content to filter out portions of the content matching the negative content indicators or present portions of the content matching the positive content indicators, thereby generating modified content that is output to the patient via a content access application executing on a computing device associated with the patient.

Abstract: The capture and search of application properties for applications installed on virtual machines, includes obtaining a plurality of logs on a plurality of virtual machine images coupled to a network. For each given virtual machine image of the plurality of virtual machine images, the method includes: obtaining one or more logs on the given virtual machine image of the plurality of virtual machine images; analyzing each given log of the one or more logs to identify one or more concepts related to a set of application properties for one or more applications installed on the given virtual machine image; associating the one or more concepts and the set of application properties with an identity of the given virtual machine image; and storing the association of the one or more concepts and the set of application properties with the identity of the given virtual machine image in a virtual machine catalogue.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for efficiently scheduling referral appointments. The computer-implemented method may include receiving text data corresponding to a referral request; determining patient data based on the text data comprising patient demographic data, patient complaint data, patient medication data, and patient history data; determining doctor data based on the text data comprising sending doctor data and receiving doctor; determining a patient treatment time based on the patient data and the receiving doctor data; sending an appointment request including the patient treatment time to a receiving doctor computing device; and receiving an appointment confirmation including the patient treatment time, wherein the appointment confirmation indicates acceptance of the appointment request by the receiving doctor.

Abstract: Embodiments include methods, system and computer program products for extracting attributes from embedded table structures in a document. Aspects include identifying a table in the document and identifying one or more headers of the table by locating co-occurring attributes in the table. Aspects also include identifying a plurality of values in the table and creating an annotation for each of the plurality of values value in the table, wherein each annotation includes text extracted from the one or more headers that correspond to the location of the value in the table.

Abstract: Techniques for natural language processing based on user context include identifying a context of a user and responsive to receiving a request from the user intended for processing by a natural language processing (NLP) model, accounting for the context of the user in relation to the request. A result from the NLP model having accounted for the context of the user is provided.

Abstract: Examples described herein provide a computer-implemented method that includes performing a text analysis on unstructured text to identify a restriction associated with a subject. The method further includes identifying an environmental requirement associated with the subject based at least in part on the restriction. The method further includes identifying one or more devices associated with the subject based at least in part on the environmental requirement. The method further includes receiving data from the one or more devices. The method further includes determining device usage information based at least in part on the data. The method further includes determining a compliance of the subject to the restriction based at least in part on the device usage information. The method further includes, responsive to determining non-compliance of the subject, changing a function of the one or more devices.

Abstract: Aspects of the present disclosure include receiving, by a processor, a procedure code for a patient, wherein the procedure code is associated with a medical device, obtaining, by the processor, patient medical data for the patient, analyzing the patient medical data to identify one or more patient medical conditions and one or more other procedure codes, determining a risk score for the procedure code based on the one or more patient medical conditions and the one or more other procedure codes, and enacting an action based at least in part on the risk score for the procedure code

Abstract: A computer-implemented method includes receiving, by a natural language processing (NLP) annotator, an input text that is to be annotated. The method further includes determining, by the NLP annotator, a user setting that indicates an aggressiveness level of annotation to be used to annotate the input text. The method further includes selecting, by the NLP annotator, from a plurality of dictionaries, a first dictionary based at least in part on the aggressiveness level. The method further includes generating, by the NLP annotator, annotated text of the input text based at least in part on the first dictionary. The method further includes outputting, by the NLP annotator, the annotated text.

Abstract: According to a computer-implemented method, an output is received resulting from natural language processing of unstructured text. In the output different phrases of the unstructured text are categorized into classes. A different visual distinction is generated for at least one class. A generated visual distinction is applied to phrases in the unstructured text based on their respective class. The unstructured text is displayed such that phrases in the unstructured text that correspond to the at least one class appear with the corresponding visual distinction indicative of that class.

Abstract: A mechanism is provided for implementing an emergency response cognitive computing system. The emergency response cognitive computing system identifies a first party about which a communication is being performed between a caller and the emergency response system. The emergency response cognitive computing system generates, in real-time, a transcript of the communication as the communication is being conducted. The emergency response cognitive computing system performs natural language processing on the transcript of the communication to identify portions of content corresponding to medical concepts. The emergency response cognitive computing system processes patient information to identify elements of the patient information referencing concepts corresponding to medical concepts. The emergency response cognitive computing system outputs the elements of the patient information to a second party involved in the communication.