Abstract: A method, computing platform, and computer program product are provided for evaluating data quality during a clinical trial. A computing platform receives, for a clinical trial, study design information including a set of parameters and corresponding parameter values related to data quality of the clinical trial. During the clinical trial, the computing platform receives query-related information associated with queries from at least some of a plurality of participants of the clinical trial. The computing platform applies the study design information and the query-related information to at least one trained machine learning model to calculate a predicted data quality score indicating data quality for the clinical trial. At least one suggestion for improving the data quality is determined and the predicted data quality score and the at least one suggestion for improving the data quality are output.

Abstract: 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.

Abstract: A method, computing platform, and computer program product are provided. A computing platform receives, for a clinical trial, study design information including a set of parameters related to a success of a clinical trial and factors related to a relevance of the parameters. The computing platform provides multiple sets of varying parameter values. At least one trained machine learning model is applied to the study design information including the sets of parameter values to predict multiple success scores including a predicted overall success score. The computing platform outputs the provided set of values for the set of parameters that are associated with the at least one trained machine learning model predicting a best overall success score for the clinical trial.

Abstract: A method, computing platform, and computer program product are provided for monitoring a clinical trial. A computer platform receives, for the clinical trial, study design information including a set of parameters and corresponding parameter values related to travel constraints of a subject for the clinical trial. The computer platform applies the study design information and the corresponding parameter values to a trained machine learning model to calculate a predicted travel score indicative of a travel burden for the subject. When the travel score fails to satisfy a travel score threshold, the computer platform determines at least one suggestion for adjusting the travel score and the at least one suggestion is output. The computer platform outputs the predicted travel score.

Abstract: A method, computing platform, and computer program product are provided for evaluating data quality during a clinical trial. A computing platform receives, for a clinical trial, study design information including a set of parameters and corresponding parameter values related to data quality of the clinical trial. During the clinical trial, the computing platform receives query-related information associated with queries from at least some of a plurality of participants of the clinical trial. The computing platform applies the study design information and the query-related information to at least one trained machine learning model to calculate a predicted data quality score indicating data quality for the clinical trial. At least one suggestion for improving the data quality is determined and the predicted data quality score and the at least one suggestion for improving the data quality are output.

Abstract: A method, computing platform, and computer program product are provided. A computing platform receives, for a clinical trial, study design information including a set of parameters related to a success of a clinical trial and factors related to a relevance of the parameters. The computing platform provides multiple sets of varying parameter values. At least one trained machine learning model is applied to the study design information including the sets of parameter values to predict multiple success scores including a predicted overall success score. The computing platform outputs the provided set of values for the set of parameters that are associated with the at least one trained machine learning model predicting a best overall success score for the clinical trial.

Abstract: A method, computing platform, and computer program product are provided for monitoring a clinical trial. A computer platform receives, for the clinical trial, study design information including a set of parameters and corresponding parameter values related to travel constraints of a subject for the clinical trial. The computer platform applies the study design information and the corresponding parameter values to a trained machine learning model to calculate a predicted travel score indicative of a travel burden for the subject. When the travel score fails to satisfy a travel score threshold, the computer platform determines at least one suggestion for adjusting the travel score and the at least one suggestion is output. The computer platform outputs the predicted travel score.

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: 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.

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 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, system, and computer usable program product for location based security over wireless networks are provided in the illustrative embodiments. A location of a data processing system is determined based on information about a network. A security policy is selected based on the location. The security policy is applied to the data processing system such that the data processing system is configured in a security configuration for using the network while maintaining security according to the security policy.

Abstract: An invention is disclosed for dynamically changing the predefined execution sequence of steps or tasks in a computerized process while it is being performed by permitting one or more task(s) to be specified as necessary when other task(s) have been completed. Specifically, a product, method and system is provided for using a “workflow engine” software program acting in combination with a “decision-making layer” program interface between the workflow engine and the process model to determine the normal or (“default”) next step in the process and all other possible process steps that can be permissibly completed after execution of the current step or task.