International Business Machines Corporation

Other

Murthy V Devarakonda

Norbert George Vogl

Rafah A Hosn

Yu Deng

Konstantinos Magoutis

Nithya Rajamani

Birgit Monika Pfitzmann

Nikolai A Joukov

Andrzej P Kochut

Hidayatullah Habeebullah Shaikh

Jonathan Paul Munson

Michael Reuben Head

Ajay Mohindra

Ching-Huei Tsou

Manoj N Kumar

Ruchi Mahindru

Kaladhar Voruganti

Soumitra Sarkar

Vijay Krishnarao Naik

Bret W Lehman

Deborra Jean Zukowski

Ramani Ranjan Routray

Mudhakar Srivatsa

Kang-Won Lee

Sandeep Gopisetty

David Michael Chess

Ian N Whalley

Alla Segal

Chung-hao Tan

Avijit Saha

Brad L Brech

David Alson George

Alain Charles Azagury

Jimmy Paul Strickland

William A Kish

Gauri Sudhir Shah

Shaya Potter

Jack P Gelb

Keith C Goodman

Preethi Raghavan

Keerthana Boloor

Hendrik F Hamann

Jonathan Lenchner

Dakshi Agrawal

Seraphin Bernard Calo

Jeffrey Owen Kephart

Daniel Manuel Dias

Marc Adam Kaplan

Kenneth James Barker

Rajarshi Das

Christopher L Molloy

Siddharth A Patwardhan

Apratim Purakayastha

Stacey C Ramos

Paul C Tang

Lawrence H Thompson

Tony A Bentivegna

Dongyang Zhang

Arun Kwangil Iyengar

David Angelo Ferrucci

Sambit Sahu

Mahesh Viswanathan

Hui Lei

Wlodek Wlodzimierz Zadrozny

Chatschik Bisdikian

Rahul Ghosh

Lorraine M Herger

Akshat Verma

Steve Richard White

Eric William Brown

Rajat Mukherjee

John Martin Prager

Jie Zheng

John Timothy Robinson

Giovanni Pacifici

Richard Pervin King

William Carlisle Arnold

Daniela Rosu

Kamal Bhattacharya

Upendra Sharma

William H Tetzlaff

Jason Christopher Young

James John Seeger, Jr

German Sergio Goldszmidt

Rohit Jain

Matthew A Markley

Bharath Dandala

Sarah J Miller

Kunwadee Sripanidkulchai

Guerney Douglass Holloway Hunt

John K Baker

Michael Tacci

Patrick B Heywood

Steve N Lee

Anthony J Zlotek

James Rubas

Graeme Neville Dixon

Ananya Aniruddha Poddar

Safa Messaoud

Frank Stewart Filz

Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) (and generally electronic records) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.

Electronic medical record summary and presentation

A clinical information extraction and training mechanism is provided for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

Identifying information in plain text narratives EMRs

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a question prediction and answering engine for predicting questions a medical professional is attempting to answer. An interaction monitoring component monitors interaction of a medical professional with a patient electronic medical record (EMR). A question selection component selects a set of questions the medical professional is attempting to obtain an answer to from the patient EMR. The question prediction and answering engine analyzes the patient EMR to generate a set of answers to the set of questions from at least a portion of the patient EMR and outputs a report correlating the set of questions and the set of answers to the medical professional.

Automated prediction and answering of medical professional questions directed to patient based on EMR

A method, system and computer program product are disclosed for searching for data. In one embodiment, the invention provides a method comprising identifying a query and a search scope including a set of specified entities; and for each of these entities, estimating a number of documents that would be identified in a search through the entity to answer the query. On the basis of this estimating, a subset of the entities is formed. The query and this subset of entities are sent to a search engine to search the subset of entities to answer the query. In one embodiment, the estimating includes collecting statistical information from queries to build up a historical cache using heuristics or machine learning techniques, wherein the query includes a key word and a scope, and the historical cache contains a maximum number of returned results for an entity given the queries executed.

Using historical information to improve search across heterogeneous indices

A system and method for cost-based migration planning includes determining cost factors and constraints associated with at least one of migrating and transforming a plurality of components. A cost-based model is constructed using the cost factors and constraints. The cost-based model is applied to groupings of the plurality of components to provide a cost-based determination grouping scheme in accordance with the constraints. The grouping scheme is output to provide migration waves for migrating or transforming the plurality of components.

Cost-based migration waves planning

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.

Medical record problem list generation

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a relevant information graphical presentation engine for providing a graphical user interface (GUI) that presents information from a patient electronic medical record (EMR). The relevant information graphical presentation engine generates a GUI presenting patient EMR data pertinent to a condition of the patient. The relevant information graphical presentation engine identifies a change in treatment recorded in the patient EMR, submits a question to a healthcare cognitive system to query the patient EMR for a reason for the change in treatment, and receives at least one answer from the healthcare cognitive system identifying of a reason portion of the patient EMR that provides a reason for the change in treatment. The relevant information graphical presentation engine generates a GUI element that references the reason portion of the patient EMR and inserts the GUI element in the GUI in association with the information from the patient EMR pertinent to the outcome. The relevant information graphical presentation engine outputs the GUI to the healthcare professional.

Contextual EMR based dashboard graphical user interface elements

Methods and systems for evaluating compatibility of a cloud of computers to perform one or more workload tasks. One or more computing solution aspects are determined that corresponding to one or more sets of workload factors, where the workload factors characterize one or more workloads, to characterize one or more computing solutions. The workload factors are compared to the computing solution aspects in a rule-based system to exclude computing solutions that cannot satisfy the workload factors. A computing solution is selected that has aspects that accommodate all of the workload factors to find a solution that accommodates the one or more individual workloads.

Workload-to-cloud migration analysis based on cloud aspects

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.

Clinically relevant medical concept clustering

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.

Self-training of question answering system using question profiles

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.

Information theory based result merging for searching hierarchical entities across heterogeneous data sources

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.

Automated timeline completion using event progression knowledge base

Methods, devices, and systems (for outputting a case summary) receive an electronic medical record (EMR) for the medical patient, extract medical data from the EMR, provide a list of medical problems relevant to the EMR, identifying relations between the medical problems and the medical data using a question-answering (QA) system, and output the clinical summary for the EMR. The clinical summary comprises the list of medical problems, the medical data, and the relations.

Growing community of inventors

Peekskill, NY, United States of America

Murthy V Devarakonda