Abstract: One embodiment of a method for automated decision making includes receiving a first set of features associated with a decision in a workflow, generating, using a trained causal inference machine learning model that predicts one or more effects of one or more actions on an outcome, a first action to perform in the workflow based on the first set of features, and transmitting one or more messages to one or more computing devices based on the first action.

Abstract: Characterizing subpopulations by their response to a given exposure relative to an alternative. Data for a set of subjects is received, including two exposures, two outcomes, and a set of characteristics. For a number of subsets an outcome model that estimates the probability of an outcome, given an exposure and the characteristics, is trained; an individual odds ratio (iOR), based on the outcome model, is computed; a sparse model that classifies subjects into a high-iOR group or another group is trained; the characteristics used in the sparse model are recorded; and, based on the recorded characteristics, a primary set of characteristics is selected. Another outcome model, based on the set of subjects and the characteristics, is trained. An iOR based on the other outcome model is computed. A sparse model for classifying subjects into a high-iOR group or another group, using the primary set of characteristics, is trained.

Abstract: A computer-implemented method and apparatus for supporting decisions in sequential clinical risk assessment examinations, the method comprising receiving one or more first test results and a question, both associated with a patient; and assessing by a processor associated with a computing platform, information gain provided by a second test which may be performed for the patient, as the conditional mutual information between a second test and the question, using the first test result.

Abstract: A computer-implemented method and apparatus for supporting decisions in sequential clinical risk assessment examinations, the method comprising receiving one or more first test results and a question, both associated with a patient; and assessing by a processor associated with a computing platform, information gain provided by a second test which may be performed for the patient, as the conditional mutual information between a second test and the question, using the first test result.

Abstract: A method for generating reference state information, the method includes: comparing between at least one sequence of block identifiers that are accessed during a test period and at least one corresponding reference sequence of block identifiers to provide a comparison result; determining whether to generate reference state information in response to the comparison result; and generating reference state information if determining to generate the reference state information.

Abstract: A method for generating reference state information, the method includes: comparing between at least one sequence of block identifiers that are accessed during a test period and at least one corresponding reference sequence of block identifiers to provide a comparison result; determining whether to generate reference state information in response to the comparison result; and generating reference state information if determining to generate the reference state information.