International Business Machines Corporation

Sinoeast Concept Limited

Merative US L.p.

Richard Joseph Stevens

Richard D Dettinger

Daniel Paul Kolz

Eric W Will

Jeffrey Wayne Tenner

Fernando Jose Suarez Saiz

Frederick Allyn Kulack

John Edward Petri

Adam Clark

Shannon Everett Wenzel

Mark Gregory Megerian

Cale T Rath

Joel C Dubbels

William Craig Rapp

Thomas John Eggebraaten

Andrew Jason Lavery

Eric L Erpenbach

Jennifer L LaRocca

David Alan Wall

Michael Britt

Kathryn Lamont Whaley

Terrence Ross O'Brien

Janice R Glowacki

Corville O Allen

Aspen L Payton

Jeffrey K Huebert

Adam Charles Lange-Pearson

Thomas P Pitzen

Peter John Johnson

Travis Michael Drucker

Christine A Grev

Ikhua Tong

Tomer Kol

Patrick Michael Wildt

Corey Sanders

Lakshminarayanan Krishnamurthy

Peter Frederick Haggar

Erik Edward Voldal

Stephan Joseph Roorda

Jennifer Lynn La Rocca

Leah Rae Smutzer

Judy I Djugash

Craig Alan Bennett

Karen A Witting

Fred A Kulack

Michael P Koranda

Dane A Johnson

Richard L Stillwell

Dan P Kolz

Jeffery W Tenner

Richard D S Dettinger

Effron F D Esseiva

Richard Dean Dettinger

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.

Determining user interactions with natural language processor (NPL) items in documents to determine priorities to present NPL items in documents to review

Techniques for granular analysis of a knowledge graph are provided. A profile comprising a plurality of attributes is received, and a knowledge graph is analyzed to identify a plurality of therapies, based on the plurality of attributes. A document that is relevant to a first therapy of the plurality of therapies is identified, and a criterion stated in the document is determined. Further, an aggregate value is determined for the criterion, based on a plurality of participants associated with the document, wherein the first aggregate value represents attributes of the plurality of participants. A weight is generated for the document, based at least in part on the plurality of attributes and the aggregate value. A score is generated for the first therapy, based at least in part on the weight, and an optimal therapy is determined, from the plurality of therapies, based in part on the score.

Cognitive analysis of data using granular review of documents

Techniques for identifying missing evidence are provided. A plurality of documents, each comprising digitally encoded natural language text data, is received. The plurality of documents is processed to determine a plurality of pair-wise comparisons between a plurality of therapies, where each of the plurality of pair-wise comparisons indicate a relative efficacy of at least one therapy in the plurality of therapies, as compared to at least one other therapy in the plurality of therapies. A knowledge graph is generated based at least in part on aggregating the plurality of pair-wise comparisons, and the knowledge graph is analyzed to identify one or more knowledge gaps within the knowledge graph. Finally, at least an indication of the identified one or more knowledge gaps is output.

Identifying knowledge gaps utilizing cognitive network meta-analysis

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

Techniques for evaluating dynamically modified plans are provided. A selection of a treatment plan template is received, where the treatment plan template specifies a plurality of treatment stages, where each treatment stage defines a plurality of treatment options. A plurality of modifications to the treatment plan template is generated. It is determined, for each respective modification of the plurality of modifications, whether the respective modification is permissible, based on one or more predefined institutional criteria. Upon determining that a first modification of the plurality of modifications is permissible, a first treatment plan is generated based on the first modification to the treatment plan template. Further, a first predicted efficacy measure is generated for the first treatment plan based on analyzing a knowledge graph. Finally, the first treatment plan is provided, along with at least an indication of the first predicted efficacy measure.

Generating and evaluating dynamic plans utilizing knowledge graphs

Techniques for unbounded therapy evaluation are provided. A request to suggest a potential therapy based on a patient profile is received. A plurality of accepted therapies is determined based on the patient profile, where the plurality of accepted therapies is based on stored definitions obtained from one or more subject matter experts. Next, a plurality of therapy components is identified based on the plurality of accepted therapies. A plurality of potential therapies is then identified based on the plurality of therapy components, where none of the plurality of potential therapies are included in the plurality of accepted therapies. A score is generated for a potential therapy of the plurality of potential therapies based on analyzing a knowledge graph, where the score indicates a suitability of the potential therapy for a patient associated with the patient profile. Finally, the potential therapy is provided, along with an indication of the score.

Analyzing knowledge graphs with unbounded insight generation

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.

Gathering information on user interactions with natural language processor (NLP) items to order presentation of NLP items in documents

A computer-implemented method, system and computer program product for understanding information using a document analysis technique. An initial corpus of information is formed by identifying a document(s) that match a search criteria. The initial corpus of information is expanded with a set of documents containing statements with a semantic meaning within a threshold degree of similarity to a semantic meaning of statements contained within the document(s) used to form the initial corpus of information. Viewpoint(s) are then extracted from the expanded corpus of information using a natural language processing technique. A new set of documents is analyzed by identifying the subject, assertion and context statements. Assertions in the new set of documents that are within a threshold degree of agreement or disagreement with the extracted viewpoint are highlighted to assist the user in understanding how information aligns with a viewpoint.

Document analysis technique for understanding information

Techniques for cognitive analysis of documents are provided. A document corresponding to a clinical trial is received. Criteria are identified for the clinical trial based on processing the electronic document using natural language processing (NLP) techniques, where the criteria specify attributes of individuals that are eligible for the trial. Further, the criteria are analyzed using NLP techniques to determine a respective confidence level for each respective criterion, where the confidence level indicates a degree of certainty that the criterion is applicable to the trial. A knowledge graph is generated based at least in part on the clinical trial, where the confidence level assigned to each criterion is included in the knowledge graph for subsequent use. Finally, one or more therapies in the knowledge graph are selected to be recommended, based on a patient profile and the confidence level assigned to each of the criteria.

Cognitive analysis of criteria when ingesting data to build a knowledge graph

Techniques for cognitively interpreting comparative statements are provided. A corpus of documents associated with treatment of a first medical disorder is received. Relative efficacies for a plurality of therapies are determined by, for at least a first respective document in the corpus of documents, identifying and extracting, using one or more natural language processing techniques, a conclusion specified within natural language text of the respective document, where the conclusion includes a comparison between two or more therapies of the plurality of therapies, determining a sentiment of the extracted conclusion, with respect to at least one therapy of the plurality of therapies, and identifying one or more cohorts of patients that are associated with the conclusion. A knowledge graph is generated based on the relative efficacies.

Extraction, representation, and cognitive interpretation of medically relevant evidence

According to embodiments of the present invention, medical treatment outcomes are simulated. A system receives a request to determine a medical treatment pertaining to a medical condition of a patient. The request is applied to one or more models, via a processor, to simulate outcomes for a plurality of different medical treatments for the medical condition, wherein the one or more models account for impacts of the plurality of different medical treatments on use of medical treatments awaiting future approval. The medical treatment is determined from the simulated outcomes, via a processor, with a desired level of impact on the medical treatments awaiting future approval based on characteristics of the patient. Methods and computer readable media are also provided herein for simulating medical treatment outcomes.

Cognitive systems for generating prospective medical treatment guidance

Gathering information on user interactions with natural language processor (NLP) items to determine an order in which to present NLP items

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.

Natural language processing review and override based on confidence analysis

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.

Cohort consensus approach to manufacturing watson Q and A pipeline training cases from historical data

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Mantorville, MN, United States of America

Richard Joseph Stevens