International Business Machines Corporation

Optum, Inc.

Sinoeast Concept Limited

Globalfoundries Inc.

Merative US L.p.

Mark Gregory Megerian

Thomas John Eggebraaten

John Edward Petri

Richard Joseph Stevens

Adam Clark

Joel C Dubbels

Charles J Archer

Brian E Smith

Joseph D Ratterman

Kevin Spencer Barker

Marie Louise Setnes

Samuel J Miller

William Craig Rapp

Richard D Dettinger

Paul William Coteus

Michael A Blocksome

Alan G Gara

Todd Edward Takken

Amanda Peters

Scott Robert Carrier

Martin Ohmacht

Thomas M Gooding

Mark Edwin Giampapa

Scott N Gerard

Eric W Will

Rudolf Adriaan Haring

Mario J Lorenzo

Jennifer Lynn La Rocca

Brant L Knudson

Ralph E Bellofatto

Thomas A Budnik

William Maupin Stockdell

Michael K Gschwind

Gregory J Boss

Valentina Salapura

Dong Chen

Philip Heidelberger

Adam James Muff

Bruce Martin Fleischer

Alexandre E Eichenberger

Robert William Wisniewski

John A Gunnels

Burkhard Steinmacher-Burow

Kathryn Mary O'Brien

Matthias Augustin Blumrich

Sameh W Asaad

Shawn Anthony Hall

Thomas Winters Fox

John Kevin O'Brien

Chen-Yong Cher

Barry Marshall Trager

Roberto Delima

Peng Wu

Charles David Wait

Sameer Kumar

James Anthony Marcella

Todd Alan Inglett

George L Chiu

Krishnan Sugavanam

Gerard Vincent Kopcsay

Jeffrey John Parker

Fernando Jose Suarez Saiz

Robert Matthew Senger

Noel A Eisley

Craig Brian Stunkel

David L Satterfield

Douglas R Miller

Yutaka Sugawara

Amith R Mamidala

Kristan D Davis

Jose R Brunheroto

Michael B Mundy

Matthew Roger Ellavsky

John B Gordon

Robert E Walkup

James Lee Van Oosten

Michael W Schroeder

Gabor J Dozsa

Ruth Janine Poole

Peter Boyle

Adam M Tate

Patrick Michael Wildt

Verlyn Michael Fischer

Norman Christ

Kahn C Evans

Paul V Allen

Frances E Stewart

Alfred Thomas Watson, Iii

Rick A Hamilton, Ii

Corville O Allen

Brian Robert Muras

Peter Frederick Haggar

Michael D Pfeifer

Andrew Thomas Tauferner

Paul Gregory Crumley

Jon Kevin Muse

Stephan Joseph Roorda

Don Darrell Reed

Richard A Swetz

Leah Rae Smutzer

There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes identifying a first predictive entity embedding for the first predictive entity and a second predictive entity embedding for a second predictive entity; determining, using a similarity determination machine learning model and based at least in part on the first predictive entity embedding and the second predictive entity embedding, a predicted cross-entity similarity measure; and performing one or more prediction-based actions based at least in part on the predicted cross-entity similarity measure.

Cross-entity similarity determinations using machine learning frameworks

Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.

Machine-learning-based predictive behavioral monitoring

There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.

Graph-based predictive inference

Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Dynamic creation and manipulation of data visualizations

Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.

Generating proactive audiovisual queries using virtual assistant software applications

Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Negative inferences in machine learning treatment selection

A method, system, and computer program product are provided for displaying query items (e.g., patient attributes) and answers (e.g., treatment recommendations) by performing a ranking analysis of query items by running a cognitive analysis comparison of each marginal answer confidence improvement metric for each unspecified query item in the first plurality of query items to rank the query items in sorted order from largest to smallest marginal answer confidence improvement metric.

Cognitive computer assisted attribute acquisition through iterative disclosure

A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.

Customized aggregate scoring using machine learning

Embodiments provide for reduced user input requests by identifying predefined diagnosis paradigms; creating a synthetic diagnosis paradigm via a machine learning process based on prior selections of action plans recommended by the predefined diagnosis paradigms and values entered therefor, wherein the synthetic diagnosis paradigm identifies the action plans to treat the plurality of conditions based on a subset of the attribute inputs used by the predefined diagnosis paradigms; generating a graphical user interface (GUI) to prompt input for values for the subset of attribute inputs; in response to receiving the values for the subset of attribute inputs, identifying at least one condition according to the predefined and synthetic diagnosis paradigms; and displaying the action plans in the GUI in association with the synthetic and predefined diagnosis paradigms according to evaluations of the action plans based on the respective logical structures.

Reducing input requests in response to learned user preferences

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.

Medical condition independent engine for medical treatment recommendation system

Managing answers in a question-answering environment is disclosed. Managing answers in the question-answering environment can include sorting, based on a set of answer categories for a subject matter, a first set of answers into a first answer category and a second set of answers into a second answer category. Managing answers in the question-answering environment can include determining, using the subject matter, a first category sequence including the first answer category and the second answer category, and establishing, based on the first category sequence, a first answer sequence established from a portion of the first set of answers from the first answer category and a portion of the second set of answers from the second answer category.

Answer management in a question-answering environment

According to an embodiment of the present invention, a system filters a collection of application programming interfaces based on input data representing information of a document to be processed, and generates a pipeline of filtered application programming interfaces. Each filtered application programming interface is sequentially executed within the pipeline. The document may be processed through the pipeline and visualizations based on the processing of the document may be generated, allowing the pipeline to be refined to produce desired output data selected within the visualizations. Embodiments of the present invention further include a method and computer program product for generating a pipeline of filtered application programming interfaces in substantially the same manner described above.

Dynamically generating a service pipeline comprising filtered application programming interfaces

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.

Extending medical condition base cartridges based on SME knowledge extensions

Growing community of inventors

Rochester, MN, United States of America

Mark Gregory Megerian