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 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 editing text for sentiment that includes identifying a first block of text for use as a template modeling; and employing sentiment analysis engine (SAE) of the first block of text to establish a model writing style. The method can continue with parsing a second block of text into user text segments for analysis for a writing style of a user; and comparing with a sentiment comparison engine (SCE) the text segments for analysis of user writing style with the model writing style of the user are changed using a plurality of text editing rules. The text editing rules are applied to and change the writing style of user to be substantially similar with the model writing style. The method may continue with editing the second block of user text with a sentiment editor using the plurality of text editing rules to provide that the second block of user text is presented in the model writing style; and changing the second block of user text to align with the model writing style.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: A method, a processing device, and a computer program product are provided. Unstructured text may be analyzed to identify medical condition information of multiple occurrences of a medical condition for at least one subject. Times and geographic locations corresponding to the multiple occurrences of the medical condition may be obtained. Environmental information that corresponds to the times and the geographic locations of the multiple medical condition occurrences, may be retrieved. Correlations between the medical condition information and the retrieved environmental information for the at least one subject may be determined. Environmental factors affecting the medical condition, based on the determined correlations, are identified.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: Detecting and incorporating formatting characteristics within natural language processing analytics. Source documents are ingested and the markup formatting language is identified by the program. Once identified, the markup language is parsed and examined for formatting characteristics, embedded notes, comments and other metadata. The formatting characteristics of the plain text are extracted, along with the plain text, and converted into a common analysis structure (CAS), or CAS-equivalent structure, which annotates the natural language text together with its respective formatting characteristics. The CAS or CAS-equivalent structures are stored and sent to a natural language processing pipeline for further analysis via complex algorithms and rules. The natural language processing results data are curated to reflect meaningful analysis of the extracted CAS or CAS-equivalent structure.

Abstract: A method for editing text for sentiment that includes identifying a first block of text for use as a template modeling; and employing sentiment analysis engine (SAE) of the first block of text to establish a model writing style. The method can continue with parsing a second block of text into user text segments for analysis for a writing style of a user; and comparing with a sentiment comparison engine (SCE) the text segments for analysis of user writing style with the model writing style of the user are changed using a plurality of text editing rules. The text editing rules are applied to and change the writing style of user to be substantially similar with the model writing style. The method may continue with editing the second block of user text with a sentiment editor using the plurality of text editing rules to provide that the second block of user text is presented in the model writing style; and changing the second block of user text to align with the model writing style.

Abstract: A method, a processing device, and a computer program product are provided. At least one processing device correlates textual medical information related to the subject with characteristics of an image of a medical condition of the subject to generate a subject signature. The at least one processing device compares the subject signature with multiple reference signatures to determine at least one reference signature corresponding to the subject signature. Each reference signature is associated with a corresponding medical condition and is generated by correlating textual medical information regarding the corresponding medical condition with characteristics of an image of the corresponding medical condition. The at least one processing device identifies the medical condition of the subject based on the medical conditions associated with the determined at least one reference signature. Information is provided regarding the identified medical condition of the subject.

Abstract: Generating structured queries from natural language text may include receiving, using a processor, a natural language text input directed to a database management system and, using the processor, performing natural language processing on the natural language text input using an Unstructured Information Management Architecture. The natural language processing may annotate the natural language text input according to a structure of the database management system. A database operation and query elements may be determined using a processor from the annotated natural language text input. A structured query may be created, using the processor, for the database management system that implements the database operation using the query elements.

Abstract: Generating structured queries from natural language text may include receiving, using a processor, a natural language text input directed to a database management system and, using the processor, performing natural language processing on the natural language text input using an Unstructured Information Management Architecture. The natural language processing may annotate the natural language text input according to a structure of the database management system. A database operation and query elements may be determined using a processor from the annotated natural language text input. A structured query may be created, using the processor, for the database management system that implements the database operation using the query elements.

Abstract: A computer system gleans data from patient records and clinical trial descriptions using NLP techniques. NLP annotation data is used to generate clinical trial feature vectors and patient feature vectors. Clinical trial feature vectors and patient feature vectors are compared to match appropriate patient candidates with clinical trial openings.

Abstract: A method for providing annotation summaries for annotations is provided. The method may include receiving annotations associated with analyzed unstructured data. The method may further include sorting the received annotations. Additionally, the method may include receiving focal points on the analyzed unstructured data. The method may also include extracting the sorted annotations associated with the focal points. The method may further include normalizing terms and phrases associated with the extracted annotations. The method may also include determining topics based on the normalized terms and phrases associated with the extracted annotations. The method may further include grouping the extracted annotations based on the determined topics. The method may also include summarizing the grouped annotations to generate a summarized annotation. The method may further include replacing the extracted annotations with the summarized annotation.

Abstract: A method for providing annotation summaries for annotations is provided. The method may include receiving annotations associated with analyzed unstructured data. The method may further include sorting the received annotations. Additionally, the method may include receiving focal points on the analyzed unstructured data. The method may also include extracting the sorted annotations associated with the focal points. The method may further include normalizing terms and phrases associated with the extracted annotations. The method may also include determining topics based on the normalized terms and phrases associated with the extracted annotations. The method may further include grouping the extracted annotations based on the determined topics. The method may also include summarizing the grouped annotations to generate a summarized annotation. The method may further include replacing the extracted annotations with the summarized annotation.

Abstract: A method for providing annotation summaries for annotations is provided. The method may include receiving annotations associated with analyzed unstructured data. The method may further include sorting the received annotations. Additionally, the method may include receiving focal points on the analyzed unstructured data. The method may also include extracting the sorted annotations associated with the focal points. The method may further include normalizing terms and phrases associated with the extracted annotations. The method may also include determining topics based on the normalized terms and phrases associated with the extracted annotations. The method may further include grouping the extracted annotations based on the determined topics. The method may also include summarizing the grouped annotations to generate a summarized annotation. The method may further include replacing the extracted annotations with the summarized annotation.

Abstract: A computer system gleans data from patient records and clinical trial descriptions using NLP techniques. NLP annotation data is used to generate clinical trial feature vectors and patient feature vectors. Clinical trial feature vectors and patient feature vectors are compared to match appropriate patient candidates with clinical trial openings.

Abstract: Detecting and incorporating formatting characteristics within natural language processing analytics. Source documents are ingested and the markup formatting language is identified by the program. Once identified, the markup language is parsed and examined for formatting characteristics, embedded notes, comments and other metadata. The formatting characteristics of the plain text are extracted, along with the plain text, and converted into a common analysis structure (CAS), or CAS-equivalent structure, which annotates the natural language text together with its respective formatting characteristics. The CAS or CAS-equivalent structures are stored and sent to a natural language processing pipeline for further analysis via complex algorithms and rules. The natural language processing results data are curated to reflect meaningful analysis of the extracted CAS or CAS-equivalent structure.

Abstract: Detecting and incorporating formatting characteristics within natural language processing analytics. Source documents are ingested and the markup formatting language is identified by the program. Once identified, the markup language is parsed and examined for formatting characteristics, embedded notes, comments and other metadata. The formatting characteristics of the plain text are extracted, along with the plain text, and converted into a common analysis structure (CAS), or CAS-equivalent structure, which annotates the natural language text together with its respective formatting characteristics. The CAS or CAS-equivalent structures are stored and sent to a natural language processing pipeline for further analysis via complex algorithms and rules. The natural language processing results data are curated to reflect meaningful analysis of the extracted CAS or CAS-equivalent structure.

Abstract: Medical data for transcription into a medical record is received. As the medical data is received, in real time: a computer-understandable meaning of text contained in the medical data can be identified by performing natural language processing on the text contained in the medical data; at least one medical transcription rule that applies to the medical data can be identified by processing the computer-understandable meaning of the text contained in the medical data; whether the medical data is incomplete can be determined by processing the computer-understandable meaning of text using the at least one medical transcription rule; and, responsive to determining that the medical data is incomplete, at least one annotation can be added to the medical record, the at least one annotation indicating data that is missing from the medical record and where in the medical record the data is missing.

Abstract: A method for providing annotation summaries for annotations is provided. The method may include receiving annotations associated with analyzed unstructured data. The method may further include sorting the received annotations. Additionally, the method may include receiving focal points on the analyzed unstructured data. The method may also include extracting the sorted annotations associated with the focal points. The method may further include normalizing terms and phrases associated with the extracted annotations. The method may also include determining topics based on the normalized terms and phrases associated with the extracted annotations. The method may further include grouping the extracted annotations based on the determined topics. The method may also include summarizing the grouped annotations to generate a summarized annotation. The method may further include replacing the extracted annotations with the summarized annotation.