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: Natural language processing includes processing unstructured data in response to a request for a conclusion to a line of inquiry to produce structured information that conforms to a computer readable information schema. Values obtained from the unstructured data are assigned to attributes of the structured information and determining a candidate conclusion to the line of inquiry based on the value-assigned attributes. A confidence score is computed that is indicative of a sufficiency of the candidate conclusion as the conclusion to the line of inquiry. One or more of the attributes are displayed to a user for modification responsive to the confidence score of the corresponding conclusion failing to meet a confidence criterion. The displayed attributes are ranked by relevance of the attribute to the corresponding conclusion. The conclusion to the line of inquiry is determined based on the modified values of the user-selected attributes.

Abstract: Provided are a computer program product, system, and method for providing information on detected user interactions with natural language processor (NLP) items to a server to use to determine an order in which to render NLP items in a user interface. Interactions with natural language processing (NLP) items in documents are detected. For each interaction with an NLP item of the NLP items in the documents, determination is made of a context attribute value for a context attribute related to the interaction with the NLP item. Then interaction information is generated indicating the NLP item and the determined context attribute value. The interaction information is sent to the server to include in user interaction information in a database.

Abstract: Provided are a computer program product, system, and method for determining user interactions with natural language processor (NLP) items in documents to determine priorities to present NLP items in documents to review. Information is received on user interactions with natural language processing (NLP) items in documents presented at least one computing device. Indication is made in a database of user interaction information on the user interactions with the NLP items indicated in the received transmitted information. The user interaction information in the database providing interaction information for NLP items in the selected document is used to determine an order of the NLP items in the selected document. Content of the NLP items in the selected document is transmitted to present to a reviewing user at a reviewing computing device in the determined order.

Abstract: Mechanisms are provided for implementing training logic for training a Question and Answer (QA) system. A training question, associated with an answer key, is received and processed by the QA system to generate a final answer to the training question and supporting evidence for the final answer based on a corpus of information. The supporting evidence is analyzed to identify one or more evidence attributes and a plurality of correct answer entries in the answer key are searched to identify a matching correct answer entry that matches the final answer. The matching correct answer entry in the answer key is augmented to include the one or more evidence attributes in an augmented answer key and the QA system is trained based on the augmented answer key.

Abstract: Methods to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Systems and computer program products to provide selective supporting evidence processing by applying a first machine learning (ML) model to a first candidate answer to generate a first confidence score that does not consider supporting evidence for the first candidate answer, determining, from a second ML model, an expected contribution of processing supporting evidence for the first candidate answer, and upon determining that the expected contribution does not exceed a specified threshold, skipping supporting evidence processing for the first candidate answer.

Abstract: Unstructured data are processed in response to a request for information to derive attributes. The attributes include a subset of variables utilized to determine the requested information. The attributes are ranked based on a set of relevance factors for determining the requested information. One or more attributes are selected for presentation to a user based on the ranking to receive modifications and produce modified attributes. The requested information is determined based on the set of variables including the modified attributes.

Abstract: Mechanisms are provided for extending a medical condition base cartridge (MCBC) based on user modifications of the MCBC. The mechanisms generate a MCBC for a medical condition at least by performing cognitive processing of natural language content in a corpus to extract patient attributes and treatment information associated with the medical condition. Information stored in the MCBC is presented to a user via at least one interface and user input is received, where the user input indicates at least one of a modification of, addition to, or deletion of, a portion of the information presented to generate an extension to the MCBC. The extension to the MCBC is stored as an expert medical condition cartridge (EMCC) in association with the MCBC in a repository. The EMCC is input to a medical cognitive system to perform a medical cognitive operation on patient information based on the EMCC.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: Natural language processing includes processing unstructured data in response to a request for a conclusion to a line of inquiry to produce structured information that conforms to a computer readable information schema. Values obtained from the unstructured data are assigned to attributes of the structured information and determining a candidate conclusion to the line of inquiry based on the value-assigned attributes. A confidence score is computed that is indicative of a sufficiency of the candidate conclusion as the conclusion to the line of inquiry. One or more of the attributes are displayed to a user for modification responsive to the confidence score of the corresponding conclusion failing to meet a confidence criterion. The displayed attributes are ranked by relevance of the attribute to the corresponding conclusion. The conclusion to the line of inquiry is determined based on the modified values of the user-selected attributes.

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: System, method, and computer program product to identify relevant features in a deep question answering system, by classifying a first case received by the deep question answering system, and, while training the deep question answering system to answer the first case, identifying a first feature in the first case, computing a first feature score for the first feature, the first feature score indicating a relevance of the first feature in generating a correct response to the first case, and, identifying the first feature as relevant in answering the classified first case upon determining that the first feature score exceeds a relevance threshold.

Abstract: Methods, systems, and computer program products are provided for auditing database access in a distributed medical computing environment including receiving from a user a query of one or more databases within the distributed medical administration computing environment; determining by an audit manager in dependence upon audit policies for the medical computing environment whether any of the results of the query require auditing access; and if any of the results of the query require auditing access, creating an audit record and storing the audit record in an audit database.

Abstract: A system rapidly produces training cases for machine based learning by automatically creating training cases from a database of historical data. The system determines a plurality of attributes relevant to each of the training cases. The system identifies a first attribute of the plurality of attributes as an issue, and a second attribute of the plurality attributes as a response to the issue. The system identifies a plurality of cohort members from the database of historical data, where each cohort member comprises cohort member attributes that match a subset of the plurality of attributes. The system analyzes the cohort member attributes of each of the plurality of cohort members to identify the most frequent responses to the issue. The system creates the training cases where each training case comprises the issue and the most frequent responses. The system then trains a machine based learning system using the training cases.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.

Abstract: A cohort analysis mechanism analyzes cohorts with similar attributes to extrapolate additional knowledge and answer a question in a question answering system. The cohort analysis mechanism identifies cohorts for an entity of the question and extracts relevant data concerning the cohorts. The cohort analysis mechanism aggregates the relevant information for evidence scoring and answer scoring to answer a question posed to the question answering system. The aggregating of the data includes combining and ranking answers from the cohorts, gathering evidence and then answering the question with the gathered evidence.

Abstract: Auditing database access in a distributed medical computing environment includes receiving from a user a query of one or more databases within the distributed medical administration computing environment; determining by an audit manager in dependence upon audit policies for the medical computing environment whether any of the results of the query require auditing access; and if any of the results of the query require auditing access, creating an audit record and storing the audit record in an audit database.

Abstract: Auditing database access in a distributed medical computing environment includes receiving from a user a query of one or more databases within the distributed medical administration computing environment; determining by an audit manager in dependence upon audit policies for the medical computing environment whether any of the results of the query require auditing access; and if any of the results of the query require auditing access, creating an audit record and storing the audit record in an audit database.

Abstract: A cohort analysis mechanism analyzes cohorts to infer one or more additional attributes for an input case to provide a refined input case to answer a question in a question answering system. The refined input case is then used to answer a question in the question answering system. The refined input case can be used in a traditional question answering flow or in a flow that again uses cohort analysis to extract relevant data to answer the question. The cohort analysis mechanism analyzes cohorts to find common attributes in the cohorts and then determines whether to infer the common attributes into the refined input case. The cohort analysis mechanism may determine to dialog with a user to confirm an inferred attribute.