Abstract: Embodiments of the invention include methods, systems, and computer program products for generating a medical problem list. A non-limiting example of the method includes receiving, by a processor, a plurality of disease categories. A disease category set that includes a plurality of top level disease categories is defined using the processor, wherein the disease category set is based at least in part upon the plurality of disease categories. The processor is used to extract a plurality of candidate training problems from an electronic patient record training set. The processor is used to assign each of the candidate training problems to the plurality of top level disease categories. The processor is used to generate a disease category model for each of the top level disease categories from the electronic patient record training set using a machine learning technique.

Abstract: Methods, systems, and devices map data of an electronic medical record to standardized medical concepts. The standardized medical concepts are defined by medical industry standards organizations. The methods, systems, and devices identify medical problem concepts, from the standardized medical concepts, generate feature values of the medical problem concepts based on features within the mapped standardized medical concepts, and weight the medical problem concepts based on the feature values according to a weighting function. These methods, systems, and devices identify medical problems as ones of the medical problem concepts that have a weighted score above a threshold, according to the weighting, and output a list of the medical problems.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for implementing an extensible support system for multiple service offerings. For example, such a support system can be a business support system which may be employed in conjunction with a cloud computing environment.

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 which are executed by the at least one processor and configure the processor to implement a medical treatment recommendation system. The medical treatment recommendation system receives a first patient electronic medical record (EMR) corresponding to a first patient. The medical treatment recommendation system analyzes the first patient EMR to detect an abbreviation within a span of content in the EMR using a plurality of abbreviation detectors. The medical treatment recommendation system identifies an abbreviation expansion for the abbreviation based on a term frequency and context of the abbreviation expansion. The medical treatment recommendation system performs an operation of the medical treatment recommendation system with regard to the span of content based on the identified abbreviation expansion.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A contextually relevant patient information extractor is provided that receives an input question directed to medical information about a patient; analyzes a patient's electronic medical records (EMRs) to identify an initial entry in the patient's EMRs corresponding to a candidate answer to the input question; analyzes a context of the patient's EMRs based on the initial entry to identify entries in the patient's EMR that are contextually connected to the initial entry; performs question answering analysis on the initial entry and entries that are contextually connected to the initial entry to identify one or more candidate answers to the input question; and outputs a final answer to the input question based on the question answering analysis.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for ontology based resource provisioning and management for services. For example, such an ontology based approach can be utilized in conjunction with a business support system which may be employed in conjunction with a cloud computing environment.

Abstract: Technical solutions are described for automatically displaying related medical entities in an electronic medical record system. An example computer-implemented method includes displaying, via a user interface, a first list of first medical entities and a second list of second medical entities. The computer-implemented method also includes receiving a selection of a first medical entity via the user interface. The computer-implemented method also includes identifying one or more second medical entities that are related to the first medical entity. The computer-implemented method also includes in response to receiving the selection, highlighting the one or more second medical entities displayed via the user interface.

Abstract: Technical solutions are described for determining a relationship between pairs of entities. An example computer-implemented method includes generating, by a processor, an association score matrix for a pair of entities, the pair including a first entity and a second entity. The computer-implemented method also includes aggregating, by the processor, the association score matrix for the pair of entities into a single vector of association scores for the pair of entities. The computer-implemented method also includes computing, by the processor, a relationship score for the pair of entities based on the single vector of association scores. The computer-implemented method also includes, in response to the relationship score crossing a predetermined threshold, indicating that the first entity and the second entity are related to each other.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a timeline generation system, for automated timeline completion. The timeline generation system executing on the processor of the computing device identifies a plurality of events in documents in a corpus of information. The timeline generation system places the plurality of events in a partial timeline data structure. The timeline generation system selects an event progression from an event progression knowledge base. The timeline generation system aligns the selected event progression to the partial timeline data structure. The timeline generation system identifies a set of events missing from the partial timeline data structure. The timeline generation system maps the set of events missing from the partial timeline data structure to the partial timeline based on the selected event progression to form a completed timeline data structure.

Abstract: The present invention embodiments are directed to methods, systems, and computer programs for identifying relations, within at least one taxonomy, between taxonomy categories and concepts extracted from electronic content. The relations represent semantic similarities for the concepts. The concepts are clustered based on the identified relations within the at least one taxonomy.

Abstract: A method and system for discovering dependencies, configurations and utilizations among IT resources are disclosed. A discovery team writes a prediscovery script without requesting credentials and sends it to a system administrator (SA) who already has necessary credentials to execute the prediscovery script. Then, the SA reviews the prediscovery script and executes the prediscovery script on a target server. While or after executing the prediscovery script, the target server generates a result of an execution of the prediscovery script and provides the result to an analysis system. The analysis system analyzes and parses the result and generates a user-friendly data (e.g., graph or spreadsheet) that represents the result. Then, the analysis system provides the user-friendly data to the discovery team. The analysis system does not require credentials and does not directly communicate with the target server except receiving the result of the executed prediscovery script from the target server.

Abstract: The present invention embodiments are directed to methods, systems, and computer programs for identifying relations, within at least one taxonomy, between taxonomy categories and concepts extracted from electronic content. The relations represent semantic similarities for the concepts. The concepts are clustered based on the identified relations within the at least one taxonomy.

Abstract: Methods, systems, and devices provide semantically relevant information by analyzing an Electronic Medical Record (EMR) having structured data and unstructured data. In the analysis, a first set of medical concepts is identified from the unstructured data in the EMR, and a second set of medical concepts is identified from the structured data in the EMR. Relationships between medical concepts in the first set of medical concepts and the second set of medical concepts are automatically identified in a medical ontology by such methods, systems, and devices.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for implementing an extensible support system for multiple service offerings. For example, such a support system can be a business support system which may be employed in conjunction with a cloud computing environment.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for implementing an extensible support system for multiple service offerings. For example, such a support system can be a business support system which may be employed in conjunction with a cloud computing environment.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for ontology based resource provisioning and management for services. For example, such an ontology based approach can be utilized in conjunction with a business support system which may be employed in conjunction with a cloud computing environment.

Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for ontology based resource provisioning and management for services. For example, such an ontology based approach can be utilized in conjunction with a business support system which may be employed in conjunction with a cloud computing environment.

Abstract: A method, system, and computer program product are disclosed for merging search results. In one embodiment, the method comprises identifying a query, splitting the query into sub-queries, and calculating information content for each of the sub-queries. This method also comprises executing each of the sub-queries to obtain a plurality of search results, and combining the search results based on the information content calculated for the sub-queries. In an embodiment, the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; and the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters. In the embodiment the combining further includes merging the clusters based on the relevance scores computed for the clusters as well as the information content calculated for the sub-queries.

Abstract: Techniques are described for scheduling received tasks in a data center in a manner that accounts for operating costs of the data center. Embodiments of the invention generally include comparing cost-saving methods of scheduling a task to the operating parameters of completing a task—e.g., a maximum amount of time allotted to complete a task. If the task can be scheduled to reduce operating costs (e.g., rescheduled to a time when power is cheaper) and still be performed within the operating parameters, then that cost-saving method is used to create a workload plan to implement the task. In another embodiment, several cost-saving methods are compared to determine the most profitable.