Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus for filtering clinical trials using machine learning techniques is disclosed. An example method generally includes receiving a first set of filters that were applied to a first plurality of clinical trials with respect to a first patient. A system determines one or more attributes of the first patient and trains a machine learning (ML) model based on the first set of filters and the one or more attributes of the first patient. The system receives a selection of a second patient, determines one or more attributes of the second patient, and generates a second set of filters by processing the one or more attributes of the second patient using the trained ML model.

Abstract: A method and apparatus for identifying contextual information related to clinical trial criteria using machine learning techniques is disclosed. An example method generally includes training a machine learning (ML) model to identify an intended respondent for a criterion. A system receives a plurality of criteria associated with a first clinical trial and determines a respective intended respondent for each of the plurality of criteria based on analyzing the plurality of criteria using the ML model. The system associates each of the plurality of criteria with an indication of the corresponding intended respondent.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the invention may be used to improve enforcement and compliance with publishing rules in an automated and provable manner. Prior to publication, documents may be processed using publishing rules (workflows) based on conceptual analysis of document content. Additionally, embodiments of the invention include a content creation system configured to provide prompt feedback on content coverage. Such a system enables the creator of information to better understand what approval requirements apply to content they create and intend to publish, as the content is being created.

Abstract: Embodiments of the invention may be used to improve enforcement and compliance with publishing rules in an automated and provable manner. Prior to publication, documents may be processed using publishing rules (workflows) based on conceptual analysis of document content. Additionally, embodiments of the invention include a content creation system configured to provide prompt feedback on content coverage. Such a system enables the creator of information to better understand what approval requirements apply to content they create and intend to publish, as the content is being created.

Abstract: Embodiments of the invention may be used to improve enforcement and compliance with publishing rules in an automated and provable manner. Prior to publication, documents may be processed using publishing rules (workflows) based on conceptual analysis of document content. Additionally, embodiments of the invention include a content creation system configured to provide prompt feedback on content coverage. Such a system enables the creator of information to better understand what approval requirements apply to content they create and intend to publish, as the content is being created.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: Evaluating candidate answers to questions in a target knowledge domain, including: identifying, by a question answering module, a plurality of evidence items in the target knowledge domain; identifying, by the question answering module, a plurality of answers to questions in the target knowledge domain; determining, by the question answering module, associations between each evidence item and one or more of the answers; receiving, by the question answering module, a question; receiving, by the question answering module, values for the one or more evidence items; and providing, by the question answering module, an answer to the question in dependence upon the associations between each evidence item and one or more of the answers and the values for the one or more evidence items.

Abstract: Embodiments of the invention allow structured data to be transferred between a source application and a service-oriented target application that process data in different formats. The data to be transferred may be divided into different sections, where each section is processed by a respective service of the target application. To transform each section of data into a format compatible with a respective service, the data may have a set of associated rules which transform data based on a set of variables. The source application may provide variable definitions for each of the variables. Each service of the target application may specify values for each of the variables. Once the variables have a specified value, a transformation engine may transform each section of the data and subsequently transfer the transformed sections of the data to respective services of the target application for processing.