Abstract: The present disclosure generally relates to the control of robotic end-effectors in order to manipulate objects. An exemplary method includes updating a classifier based on sensor data obtained at a first time and applying the updated classifier to second sensor data obtained at a second time, to assess status of a robotic end-effector with respect to one or more objects. The method further includes determining a robotic action based on the status assessed and causing a robotic device including the robotic end-effector to perform the robotic action.

Abstract: In an approach for constructing causal graphs, a processor receives data, a first set of constraints, and one or more graph parameters. A processor constructs a causal graph based on the data, first set of constraints, and one or more graph parameters. A processor generates an interactive display interface for the constructed causal graph. A processor refines the constructed causal graph using the interactive display interface.

Abstract: The present disclosure generally relates to the control of robotic end-effectors in order to manipulate objects. An exemplary method includes updating a classifier based on sensor data obtained at a first time and applying the updated classifier to second sensor data obtained at a second time, to assess status of a robotic end-effector with respect to one or more objects. The method further includes determining a robotic action based on the status assessed and causing a robotic device including the robotic end-effector to perform the robotic action.

Abstract: In an approach for constructing causal graphs, a processor receives data, a first set of constraints, and one or more graph parameters. A processor constructs a causal graph based on the data, first set of constraints, and one or more graph parameters. A processor generates an interactive display interface for the constructed causal graph. A processor refines the constructed causal graph using the interactive display interface.

Abstract: Modelling disease progression using non-clinical information proxies for clinical information, by accessing a computer-based Bayesian model of the progression of a disease, adapting the Bayesian model to include one or more clinical factors that are believed to influence progression of the disease, adapting the Bayesian model to include one or more non-clinical proxies for one or more clinical factors that are believed to influence progression of the disease, identifying interdependencies among variables of the Bayesian model based on a meta-analysis of literature associated with any of the disease, the clinical factors, and the non-clinical proxies, providing values for any of the variables of the Bayesian model, and presenting any portion of the Bayesian model via a computer-based output device.

Abstract: A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Abstract: A set of log entries is automatically inspected to determine a bug. A training set is utilized to determine clustering of log identifications. Log entries are examined in real-time or retroactively and matched to clusters. Timeframe may also be matched to a cluster based on log entries associated with the timeframe. Error indications may be outputted to a user of the system in respect to a log entry or a timeframe.

Abstract: A set of log entries is automatically inspected to determine a bug. A training set is utilized to determine clustering of log identifications. Log entries are examined in real-time or retroactively and matched to clusters. Timeframe may also be matched to a cluster based on log entries associated with the timeframe. Error indications may be outputted to a user of the system in respect to a log entry or a timeframe.

Abstract: A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.

Abstract: A set of log entries is automatically inspected to determine a bug. A training set is utilized to determine clustering of log identifications. Log entries are examined in real-time or retroactively and matched to clusters. Timeframe may also be matched to a cluster based on log entries associated with the timeframe. Error indications may be outputted to a user of the system in respect to a log entry or a timeframe.