Abstract: Provided is a process, including: obtaining test data that quantifies a patient's cardiovascular status, the test data specifying patient attributes in a plurality of cardiovascular dimensions of the patient; determining a plurality of normalized differences between the test data and target criteria in each of the cardiovascular dimensions; determining predicted-effect vectors of each of a plurality of different classes of pharmaceuticals; determining an aggregate score for each respective class of pharmaceuticals among the different classes of pharmaceuticals based on values of the corresponding predicted-effect vectors; ranking the different classes of pharmaceuticals based on the aggregate scores; and outputting a recommended sequence of pharmaceuticals to administer.

Abstract: Provided is a process, including: obtaining test data that quantifies a patient’s cardiovascular status, the test data specifying patient attributes in a plurality of cardiovascular dimensions of the patient; determining a plurality of normalized differences between the test data and target criteria in each of the cardiovascular dimensions; determining predicted-effect vectors of each of a plurality of different classes of pharmaceuticals; determining an aggregate score for each respective class of pharmaceuticals among the different classes of pharmaceuticals based on values of the corresponding predicted-effect vectors; ranking the different classes of pharmaceuticals based on the aggregate scores; and outputting a recommended sequence of pharmaceuticals to administer.

Abstract: Provided is a process, including: obtaining test data that quantifies a patient's cardiovascular status, the test data specifying patient attributes in a plurality of cardiovascular dimensions of the patient; determining a plurality of normalized differences between the test data and target criteria in each of the cardiovascular dimensions; determining predicted-effect vectors of each of a plurality of different classes of pharmaceuticals; determining an aggregate score for each respective class of pharmaceuticals among the different classes of pharmaceuticals based on values of the corresponding predicted-effect vectors; ranking the different classes of pharmaceuticals based on the aggregate scores; and outputting a recommended sequence of pharmaceuticals to administer.

Abstract: Provided is a process that includes: obtaining, with an artificial intelligence (AI) application executed by a computer system, information about a current patient condition related to heart failure; inputting, with the computer system, at least some of the obtained information about the patient condition into respective pharmaceutical-specific models to obtain candidate classes of pharmaceuticals and their respective priority scores; inputting, with the computer system, into an expert system of the AI application, the candidate classes of pharmaceuticals and their respective priority scores and, in response, determining an updated prescription.

Abstract: Provided is a process, including: obtaining test data that quantifies a patient's cardiovascular status, the test data specifying patient attributes in a plurality of cardiovascular dimensions of the patient; determining a plurality of normalized differences between the test data and target criteria in each of the cardiovascular dimensions; determining predicted-effect vectors of each of a plurality of different classes of pharmaceuticals; determining an aggregate score for each respective class of pharmaceuticals among the different classes of pharmaceuticals based on values of the corresponding predicted-effect vectors; ranking the different classes of pharmaceuticals based on the aggregate scores; and outputting a recommended sequence of pharmaceuticals to administer.

Abstract: A method and system detects potential suspicious behavior in a monitored facility. The monitored facility includes at least one point of transaction terminal. Video content of an activity occurring at the monitored facility and transaction data relating to a transaction processed at the transaction terminal are collected. The video content is correlated with the transaction data to produce correlated data. A set of user-defined rules are applied to the correlated data. Responsive to identifying a match between the correlated data and at least one rule of the set of user-defined rules, the transaction is determined to be potentially suspicious.