Abstract: A computing system is configured to receive a plurality of predefined rules from a database. The rules include a first set of predefined rules that includes a hardcoded priority value for each respective predefined rule and a second set of predefined rules that do not have a hardcoded priority value. Furthermore, each rule is associated with a keyword. The computing system determines, for the first set of predefined rules, that each respective predefined rule includes the hardcoded priority value and orders the first set of predefined rules in order of priority based on the hardcoded priority values. The computing system performs a super rule/subrule prioritization operation on the second set of predefined rules, which includes prioritizing one or more super rules over its associated one or more subrules. The computing system then performs an accuracy-based prioritization operation on the second set of predefined rules after the super rule/subrule prioritization operation.

Abstract: A computing system receives rules and transaction data from a database. The system creates a potential rule list and stores the rules in the list on the database. The system creates an ordered rule list, a rule-to-transaction lookup table, and a transaction-to-rule lookup table. The system applies each rule to each transaction and identifies each transaction for which each rule is true. The system adds each rule and the identified transactions to the lookup tables and generates a priority-ordered list of the rules iteratively until all rules have been removed from the potential rule list. In each iteration, the system calculates an accuracy of each rule based on all transactions for which the rule is true, appends the highest accuracy rules to the ordered rule list, removes the rules from the potential rule list, and removes the transactions from all entries in the rule-to-transaction lookup table.

Abstract: A computing system is configured to receive raw transaction data from a database. The computing system performs a multipattern substring matching operation on the raw transaction data using an expert system model. The computing system identifies one or more predefined keywords within one or more of description and memo data fields for each transaction data piece of a set of transaction data pieces. The computing system filters one or more predefined rules based on the identified one or more predefined keywords and assign a category label to each transaction data piece. The assigned category label is based on a highest priority rule determined to be true for the respective transaction data piece.

Abstract: A method, system and apparatus for identifying a type of device connected to I/O (Input/Output) channels of an I/O module, can involve applying a current for a predefined time duration to one or more I/O channels of an I/O module, initiating a current readback at a regular interval of time within the predefined time duration, determining values of the current readback for each of a time interval during the predefined time duration, storing the values of the current readback in a memory associated with the I/O module, determining a pattern as a function of the values of the current readback and a sample of time interval for a device connected to the one or more I/O channels, and identifying a type of the device connected to the one or more I/O channels based on a determined pattern of a current sink of the device connected to the one or more I/O channels.

Abstract: A method, system and apparatus for identifying a type of device connected to I/O (Input/Output) channels of an I/O module, can involve applying a current for a predefined time duration to one or more I/O channels of an I/O module, initiating a current readback at a regular interval of time within the predefined time duration, determining values of the current readback for each of a time interval during the predefined time duration, storing the values of the current readback in a memory associated with the I/O module, determining a pattern as a function of the values of the current readback and a sample of time interval for a device connected to the one or more I/O channels, and identifying a type of the device connected to the one or more I/O channels based on a determined pattern of a current sink of the device connected to the one or more I/O channels.

Abstract: In the pharmaceutical research and development process, it may be necessary to process large amounts of medical records or clinical literature, to ensure safety of patients consuming a drug. A pharmacovigilance system may assist in this process by efficiently and automatically processing medical records to extract information and relationships contained therein and may also form a preliminary assessment regarding a medical or clinical judgment. The pharmacovigilance system may automatically generate reports based on this information, which may be validated by trained clinicians and medical experts.

Abstract: In the pharmaceutical research and development process, it may be necessary to process large amounts of medical records or clinical literature, to ensure safety of patients consuming a drug. A pharmacovigilance system may assist in this process by efficiently and automatically processing medical records to extract information and relationships contained therein and may also form a preliminary assessment regarding a medical or clinical judgment. The pharmacovigilance system may automatically generate reports based on this information, which may be validated by trained clinicians and medical experts.