International Business Machines Corporation

Shahram Ebadollahi

Fei Wang

Jimeng Sun

Jianying Hu

Francisco Phelan Curbera

Robert K Sorrentino

Chung-Sheng Li

John Richard Smith

Ajay Mohindra

Milind R Naphade

Aleksandra Mojsilovic

Apostol Ivanov Natsev

Shubir Kapoor

Shilpa N Mahatma

Ock Kee Baek

Lexing Xie

Jelena Tesic

Gigi Yuen-Reed

Houtan Aghili

Martin S Kohn

Marie Angelopoulos

Anthony Tom Levas

Youssef Drissi

Matthew Leon Hill

Ioana Roxana Stanoi

James William Cooper

Murray Scott Campbell

Hani Neuvirth-Telem

James V Codella

Michal Rosen-Zvi

Lavi Shpigelman

Ching-Hua Chen

Si Sun

Maria Eleftherou

Stewart T Sill

Jonathan D Laserson

Noah Lee

Brian Michael Landry

Jiayu Zhou

Performing an operation comprising generating, by a computing system based on data received from a plurality of data sources, a data model describing a patient, wherein the data comprises medical history data of the patient and observation data received by the computing system during observation of at least one medical professional, generating, by the computing system and based on the model applied to the received data, a plurality of candidate diagnoses for the patient, executing, by the computing system, a plurality of simulations of a plurality of candidate treatments generated by the computing system based on the model, and identifying a first candidate treatment of the plurality of candidate treatments that reduces an uncertainty value of the data model below a threshold.

Medical diagnosis system with continuous learning and reasoning

A system provides an intervention for a user and comprises at least one processor. The system monitors behavior and context of a user to generate a behavior history. One or more models are utilized to determine an intervention for the user to induce a behavior modification, wherein the one or more models map interventions to user context and behavior and utilize the behavior history to determine an effective intervention for the user. The intervention is provided to the user and feedback is received in response to the intervention. The one or more models are updated based on the feedback. Embodiments of the present invention further include a method and computer program product for providing an intervention to a user in substantially the same manner described above.

Personalized intervention based on machine learning of behavior change states

A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.

Dynamic identification of the biomarkers leveraging the dynamics of the biomarker

An application as a service provided in a secure environment. A sandbox in a user's computing environment may be created. An application may be downloaded to the user's computing environment to run within the sandbox. Data sources associated with the user's computing environment may be searched and connectivity established with data registry of the data sources based on data description received with the application. The application may be run within the sandbox using the established connectivity. Metering may be performed to monitor usage of the application at the user's computing environment.

Healthcare as a service&#x2014;downloadable enterprise application

A system and method for a composite distance metric leveraging multiple expert judgments includes inputting a data distribution of multiple expert judgments stored on a computer readable storage medium. Base distance metrics are converted into neighborhoods for comparison, wherein each base distance metric represents an expert and each neighborhood represents an individual similarity measure of the expert. The neighborhoods are combined to leverage the local discriminalities of all base distance metrics by applying at least one iterative process to output a composite distance metric.

System and method for composite distance metric leveraging multiple expert judgments

A system and method for a composite distance metric leveraging multiple expert judgments includes inputting a data distribution of multiple expert judgments stored on a computer readable storage medium. Base distance metrics are converted into neighborhoods for comparison, wherein each base distance metric represents an expert. The neighborhoods are combined to leverage the local discriminalities of all base distance metrics by applying at least one iterative process to output a composite distance metric.

A system and method for processing data includes organizing medical information in a concept frame data structure, which is adapted to include medical measurements and related metadata. The medical information is analyzed to extract further information using information extractors and to store extracted medical information in the concept frame data structure. References are stored to appropriate visualization methods along with an associated concept in the concept frame data structure.

System and method for organizing and displaying of longitudinal multimodal medical records

A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Method and system for outcome based referral using healthcare data of patient and physician populations

A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.

Assessing practitioner value in multi-practitioner settings

Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.

Mining temporal patterns in longitudinal event data using discrete event matrices and sparse coding

The present invention is directed to a method and apparatus for assisting in rating and filtering multimedia content, such as images, videos and sound recordings. One embodiment comprises a computer implemented method for rating the objectionability of specified digital content that comprises one or more discrete content items, wherein the method includes the step of moving the specified content to one or more filtering stages in a succession of filtering stages. After the specified content is moved to a given one of the filtering stages, a rating procedure is carried out to determine whether a rating can be applied to one or more of the content items, and if so, a selected rating is applied to each of the one or more content items. The method further comprises moving content items of the specified content to the next stage in the succession after the given stage, when at least one content item of the specified content remains without rating, after the rating procedure at the given stage. When none of the content items of the specified content remains without a rating after the rating procedure has been completed at the given stage, ratings that have been respectively applied to at least some of the content items are selectively processed, in order to determine an overall objectionability rating for the specified content.

Method and apparatus for semantic assisted rating of multimedia content

A method and apparatus is disclosed that receives an input narrative in a particular media format, and produces therefrom a multimedia enriched version of the input narrative. In one embodiment, a method is provided for enriching an input narrative with multimedia content, wherein the method includes the step of selectively segmenting the input narrative to form queries pertaining to different portions or elements of the input narrative. The method further comprises retrieving multimedia artifacts from one or more multimedia repositories, wherein each retrieved multimedia artifact is associated with one of the queries, and selecting a subset of the retrieved multimedia artifacts, wherein the subset includes artifacts comprising different types of multimedia content. The input narrative is then combined with respective multimedia artifacts of the subset, in order to provide an enriched and enhanced narrative.

Method and apparatus for automatic multimedia narrative enrichment

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White Plains, NY, United States of America

Shahram Ebadollahi