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: Mechanisms are provided for performing entity differentiation. A cognitive medical system ingests a corpus of medical content having references to medical entities, and performs entity recognition on the medical content to identify the medical entities. Responsive to the cognitive medical system identifying a medical entity having a plurality of annotations for a same medical entity attribute, an entity differentiation component executes an ordered set of entity differentiation algorithms, corresponding to the medical entity, for differentiating medical entity attribute values. The entity differentiation component runs the ordered set of entity differentiation algorithms, in order, on the plurality of annotations for the attribute to generate a ranked list of medical entity attribute values corresponding to the annotations in the plurality of annotations. The cognitive medical system performs a cognitive operation on the medical entity based on the ranked list of medical entity attribute values.

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.

Abstract: A mechanism is provided that implements a medical information release mechanism. The medical information release mechanism receives, via a sensor, an input from a use of the medical information release mechanism by a patient. The medical information release mechanism performs an authentication operation on the input to determine whether the input corresponds to the patient of the medical information release mechanism in response to receiving the input sensed by the sensor. The medical information release mechanism controls a wireless communication system to send a medical information release signal to at least one other computing device in response to determining that the input corresponds to the patient of the medical information release apparatus. The medical information release signal identifies the patient and provides an authorization to the at least one other computing device to access patient information about the patient.

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: A method for de-identifying protected health information (PHI) associated with electronic medical records (EMRs) based on a common analysis structure (CAS) is provided. The method may include detecting a system event associated with a system comprising the EMRs. The method may further include in response to detecting the system event, detecting a first CAS associated with the EMRs. The method may further include extracting first CAS data associated with the first CAS, wherein the first CAS data comprises unstructured data associated with the EMRs and normalized annotations based on CAS objects that are associated with the unstructured data. The method may further include obfuscating the unstructured data associated with the first CAS. The method may also include generating a second CAS comprising the obfuscated unstructured data and a copied version of the normalized annotations, wherein the copied version of normalized annotations are correlated with the obfuscated unstructured data.

Abstract: Mechanisms are provided for resolving annotation conflicts in a portion of content. A portion of content is received and annotation operations are performed on it using a plurality of annotators. Each annotator in the plurality of annotators generates a set of annotations for the portion of content, thereby generating a plurality of sets of annotations for the portion of content. The plurality of sets of annotations are analyzed to identify a conflict between a first annotation and a second annotation in the plurality of sets of annotations. The conflict between the first annotation and the second annotation is resolved to generate a resultant set of annotations for the portion of content. The resultant set of annotations is stored in an annotations data structure associated with the portion of content.

Abstract: A technique of improving input text processing in a data processing system that answers questions includes receiving a question from a user. The question has associated supporting original input text that is utilized in answering the question. Corrective measures are applied to the supporting original input text to create supporting revised input text. The supporting revised input text documents corrective measures applied to the supporting original input text. An answer to the question is generated based on, at least in part, the supporting revised input text. The supporting original input text is recreated from the supporting revised input text based on the documented corrective measures in the supporting revised input text subsequent to generating the answer. The answer and the supporting original input text as recreated are transmitted to the user.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a cognitive natural language processing system. The cognitive natural language processing (NLP) system analyzes a portion of natural language text to identify an attribute specified in the natural language text. The cognitive NLP system analyzes the portion of natural language text to determine whether a known negation trigger is present in the natural language text in association with the attribute. In response to determining that the natural language text does not contain a known negation trigger in association with the attribute, the cognitive NLP system determines whether the attribute is negated based on instances of the attribute in other natural language content similar to the natural language text.

Abstract: The program directs a computer processor to resolve an anaphor in electronic natural language text. The program detects a plurality of entities and an anaphor in a span of parsed natural language text comprising one or more sentences, and extracts pairs of related entities based on domain knowledge. The program constructs a set of tuples, wherein each tuple is a data type comprising an anaphor, an antecedent entity (AE) appearing before the anaphor in the span of parsed natural language text, and an entity (E) appearing after the anaphor in the span of parsed natural language text, wherein the anaphor refers to the AE and relates the AE to the E. The program resolves the anaphor by determining which entity in the plurality of entities the anaphor references, using the constructed set of tuples, and selecting an AE among one or more candidate AEs.

Abstract: Mechanisms receive natural language content and analyze the natural language content to generate a parse tree data structure. The mechanisms process the parse tree data structure to identify one or more instances of candidate hypothetical spans in the natural language content. Hypothetical spans are terms or phrases indicative of a hypothetical statement. The mechanisms calculate, for each candidate hypothetical span, a confidence score value indicative of a confidence that the candidate hypothetical span is an actual hypothetical span based on a personalized hypothetical dictionary data structure associated with a source of the natural language content. The mechanisms perform an operation based on the natural language content.

Abstract: Mechanisms are provided for performing entity differentiation. A cognitive medical system ingests a corpus of medical content having references to medical entities, and performs entity recognition on the medical content to identify the medical entities. Responsive to the cognitive medical system identifying a medical entity having a plurality of annotations for a same medical entity attribute, an entity differentiation component executes an ordered set of entity differentiation algorithms, corresponding to the medical entity, for differentiating medical entity attribute values. The entity differentiation component runs the ordered set of entity differentiation algorithms, in order, on the plurality of annotations for the attribute to generate a ranked list of medical entity attribute values corresponding to the annotations in the plurality of annotations. The cognitive medical system performs a cognitive operation on the medical entity based on the ranked list of medical entity attribute values.

Abstract: The program directs a computer processor to resolve an anaphor in electronic natural language text. The program detects a plurality of entities and an anaphor in a span of parsed natural language text comprising one or more sentences, and extracts pairs of related entities based on domain knowledge. The program constructs a set of tuples, wherein each tuple is a data type comprising an anaphor, an antecedent entity (AE) appearing before the anaphor in the span of parsed natural language text, and an entity (E) appearing after the anaphor in the span of parsed natural language text, wherein the anaphor refers to the AE and relates the AE to the E. The program resolves the anaphor by determining which entity in the plurality of entities the anaphor references, using the constructed set of tuples, and selecting an AE among one or more candidate AEs.

Abstract: First content containing a plurality of list items in one or more lists can be parsed for conjunctions and implied list indicators. One or more modifications can occur at one or more conjunctions or implied list indicators. The one or more modifications can comprise one or more of expanding text, contracting text, and replacing text. The one or more modifications can generate second content conducive to natural language processing operations.

Abstract: A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a cognitive medical system. The data processing system determines a cognitive operation to be performed by the cognitive medical system. The cognitive medical system ingests a corpus of medical content. The medical content comprises references to medical entities and ingesting the corpus comprises performing entity recognition on the medical content to identify the medical entities. Responsive to identifying a given medical entity having a plurality of annotations for an attribute.

Abstract: First content containing a plurality of list items in one or more lists can be parsed for conjunctions and implied list indicators. One or more modifications can occur at one or more conjunctions or implied list indicators. The one or more modifications can comprise one or more of expanding text, contracting text, and replacing text. The one or more modifications can generate second content conducive to natural language processing operations.

Abstract: Mechanisms receive natural language content and analyze the natural language content to generate a parse tree data structure. The mechanisms process the parse tree data structure to identify one or more instances of candidate hypothetical spans in the natural language content. Hypothetical spans are terms or phrases indicative of a hypothetical statement. The mechanisms calculate, for each candidate hypothetical span, a confidence score value indicative of a confidence that the candidate hypothetical span is an actual hypothetical span based on a personalized hypothetical dictionary data structure associated with a source of the natural language content. The mechanisms perform an operation based on the natural language content.

Abstract: A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The mechanism receives a plurality of patient electronic medical records (EMRs) for a patient from a plurality of different sources. For a portion of a patient EMR record of the plurality of patient EMRs, the mechanism detects entities and analyzes a document structure of the portion of the patient EMR to identify a hierarchical structure of the portion of the patient EMR. The mechanism generates a container representation of the portion of the patient EMR based on the hierarchical structure. The mechanism placing each of the one or more sentences within the container representation based on relative position within the hierarchical structure.

Abstract: A mechanism is provided in a data processing system comprising least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a clinical decision support system. The clinical decision support system receives a plurality of patient electronic medical records (EMRs) for a patient, from a plurality of different sources. For a portion of a patient EMR record of the plurality of patient EMRs, the clinical decision support system detects entities and analyzing a document structure of the portion of the patient EMR to identify a hierarchical structure of the portion of the patient EMR. The clinical decision support system generates a container representation of the portion of the patient EMR based on the hierarchical structure. The clinical decision support system generates a set of grammatical representations of one or more relationships identified within the container representation.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a cognitive natural language processing system. The cognitive natural language processing (NLP) system analyzes a portion of natural language text to identify an attribute specified in the natural language text. The cognitive NLP system analyzes the portion of natural language text to determine whether a known negation trigger is present in the natural language text in association with the attribute. In response to determining that the natural language text does not contain a known negation trigger in association with the attribute, the cognitive NLP system determines whether the attribute is negated based on instances of the attribute n other natural language content similar to the natural language text.