International Business Machines Corporation

Merative US L.p.

Sinoeast Concept Limited

Adam Clark

John Edward Petri

Aspen L Payton

Jeffrey K Huebert

Richard Joseph Stevens

Kevin James Wendzel

Eric W Will

Mark Gregory Megerian

Michael T Kalmbach

Brian John Cragun

Michael D Pfeifer

John Stephen Mysak

Fernando Jose Suarez Saiz

Kathryn Lamont Whaley

Anthony W Eichenlaub

Corville O Allen

Daniel Paul Kolz

Joel C Dubbels

Christopher Thomas Gloe

John Christian Unterholzner

Kathryn Ann Tri

Frederick Allyn Kulack

Thomas John Eggebraaten

Kimberly Diane Kenna

Gregory Paul Andrews

Marie Louise Setnes

Janice R Glowacki

Luis Alexandro Garcia

Daniel Joseph Strauss

Lisa Wellman

Nathaniel D Lee

A present invention embodiment detects changes between documents. A machine learning classifier is trained with one or more sections within an initial document to learn classifications for the sections. The one or more sections of the initial document serve as the classifications. One or more second sections of a second document are applied to the machine learning classifier to classify the one or more second sections. The one or more second sections are mapped to the one or more sections of the initial document based on the classification of the one or more second sections.

Detecting changes between documents using a machine learning classifier

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

Methods and apparatuses for performing clinical trial editing using machine learning are provided. One example method generally includes receiving information of a first clinical trial that is being drafted and identifying, in a corpus of literature, a plurality of documents that are relevant to the first clinical trial, based on the title of the first clinical trial. The method further includes providing the plurality of documents and the plurality of criteria to a machine learning model configured to output for each respective criterion of the plurality of criteria, a confidence value for the respective criterion, receiving as output from the machine learning model the confidence value for each respective criterion and, upon determining that a first criterion has a first confidence value below a predefined threshold, prompting a user to verify the first criterion.

Clinical trial editing using machine learning

A method and apparatus for providing curated criteria to identify one or more candidates for a clinical trial is disclosed. A computer processor identifies a first input criterion for the clinical trial. The processor employs a trained first recurrent neural network (RNN) configured as an encoder to encode the first input criterion. The encoder extracts key features of the medical condition of the patient. The processor employs a trained second RNN configured as a decoder to generate a curated output criterion by processing the encoded first input criterion based on the derived key features. The processor employs a machine learning model to ingest the curated output criterion to identify the one or more candidates for the clinical trial.

Recurrent neural network to decode trial criteria

A method, computer program product, and system for a cognitive dialoguing avatar, the method including identifying a user, a target entity, and a user goal, initiating communication with the target entity, evaluating cognitively a question from a dialog with the target entity, determining cognitively an answer to the question by evaluating stored user information to progress to the user goal, communicating the determined answer to the target entity.

Voice prompt avatar

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

Method and apparatus for predicting clinical trial eligibility for patients. Embodiments include determining a current value for each of a plurality of attributes of a first patient. Embodiments include identifying a cohort of patients that are clinically similar to the first patient, based on the plurality of attributes. Embodiments include analyzing data associated with the cohort of patients to determine an attribute trend for at least a first attribute the plurality of attributes. Embodiments include generating a predicted value for the first attribute, based on the current value of the first attribute and the attribute trend for the first attribute. Embodiments include identifying a plurality of clinical trials based on the first attribute. Embodiments include generating a probability that the first patient will be eligible for each of the plurality of clinical trials at a future time, based on the predicted value for the first attribute.

Predicting clinical trial eligibility based on cohort trends

Method and apparatus for predicting beneficial clinical trials for patients. Embodiments include receiving one or more attributes of a first patient. Embodiments include selecting a potential clinical trial for the first patient, based on the one or more attributes. Embodiments include identifying a cohort of patients that are clinically similar to the first patient, based on the one or more attributes, wherein each patient in the cohort has undergone a respective trial that is either (i) the potential clinical trial, or (ii) a clinically similar clinical trial. Embodiments include determining, for each respective patient in the cohort, a respective outcome of the respective trial. Embodiments include generating a predicted outcome for the first patient, based on the respective outcomes for each patient in the cohort. Embodiments include refraining from recommending the potential clinical trial for the first patient, based on the predicted outcome.

Predicting patients who can benefit from a clinical trial

A method and apparatus for identifying implied criteria for a clinical trial is disclosed. An example method generally includes generating a training data set from a corpus of clinical trial specifications. The training data set may include at least a first sample corresponding to a first trial. The first sample may include a first feature based on one or more explicitly stated trial criteria, a second feature based on metadata describing the first trial, and a third feature based on patient data of patients associated with the first trial. A machine learning model is trained, using a supervised learning approach, based on the training data set. A system processes a second trial as an input to the trained machine learning model to determine one or more implied criteria that are not explicitly enumerated in a specification for the second trial.

Identifying implied criteria in clinical trials using machine learning techniques

Provided are techniques for a personalized, social navigation coach. While a commuter is commuting on a commute path, periodically, current conditions for the commute path are determined. One or more risk factors of having an accident based on the current conditions and a commute model are identified, where the one or more risk factors are associated with one or more icons, and where the commute model uses sensor data comprising historic data at different points in time. A graphic range is generated with an indicator that indicates an amount of risk of having an accident along the graphic range. The one or more icons and the graphic range are displayed.

Personalized social navigation coach

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

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

Provided are techniques for detecting and highlighting insightful comments in a thread of content. A context is determined for a thread of content including comments. Each of the comments in the thread of content is analyzed to determine which comments are insightful and which comments are not insightful. At least one of highlighting the comments that are insightful and hiding the comments that are not insightful are performed. Semantic relationships are used to provide supporting evidence and cross references to additional information for the comments that are related to the context.

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Mantorville, MN, United States of America

Adam Clark