Abstract: Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Abstract: Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Abstract: Mechanisms are provided for collecting and applying drug-to-drug interaction information from natural language documents. The mechanisms perform natural language processing of natural language documents in a corpus of natural language documents, to identify content in the natural language documents that describe a drug-to-drug interaction. The mechanisms categorize the drug-to-drug interaction and calculate, for a first drug in the drug-to-drug interaction, an exclusion weighting factor for use in evaluating treatments for patients that include a second drug of the drug-to-drug interaction based on the categorization of the drug-to-drug interaction. The mechanisms store the drug-to-drug interaction as a drug-to-drug interaction data structure that specifies the first drug, the second drug, the categorization of the drug-to-drug interaction, and the exclusion weighting factor associated with the drug-to-drug interaction.

Abstract: Mechanisms are provided to implement a medical condition base cartridge generator. The mechanisms ingest an electronic corpus of medical content and generate a medical condition base cartridge for a medical condition. The medical condition base cartridge is a pluggable cartridge comprising insight data structures that specify an association of clinical attributes of patients with the medical condition and a treatment for the medical condition. The mechanisms install the medical condition base cartridge as a resource for performing a cognitive operation.

Abstract: Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Abstract: Mechanisms are provided for collecting and applying drug-to-drug interaction information from natural language documents. The mechanisms perform natural language processing of natural language documents in a corpus of natural language documents, to identify content in the natural language documents that describe a drug-to-drug interaction. The mechanisms categorize the drug-to-drug interaction and calculate, for a first drug in the drug-to-drug interaction, an exclusion weighting factor for use in evaluating treatments for patients that include a second drug of the drug-to-drug interaction based on the categorization of the drug-to-drug interaction. The mechanisms store the drug-to-drug interaction as a drug-to-drug interaction data structure that specifies the first drug, the second drug, the categorization of the drug-to-drug interaction, and the exclusion weighting factor associated with the drug-to-drug interaction.

Abstract: Cognitive medical treatment recommendation mechanisms are provided. The mechanisms ingest a corpus of medical treatment content that comprises drug-to-drug interaction information. The mechanisms generate a set of drug interaction insight data structures and an initial set of candidate treatments for a medical condition of a patient based on an analysis of an electronic medical record associated with the patient. The mechanisms rank candidate treatments in the initial set of candidate treatments based on the set of drug interaction insight data structures to generate a final set of candidate treatments, where the ranking reduces rankings of candidate treatments in which drug interactions are identified in the set of drug interaction insight data structures. The mechanisms output a treatment recommendation based on the final set of candidate treatments.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: Mechanisms are provided to implement a medical condition base cartridge generator. The mechanisms ingest an electronic corpus of medical content and generate a medical condition base cartridge for a medical condition. The medical condition base cartridge is a pluggable cartridge comprising insight data structures that specify an association of clinical attributes of patients with the medical condition and a treatment for the medical condition. The mechanisms install the medical condition base cartridge as a resource for performing a cognitive operation.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: A computer system provides a way to automatically generate compound commands that perform tasks made up of multiple simple commands. A compound command generation mechanism monitors consecutive user commands and compares the consecutive commands a user has taken to a command sequence identification policy. If the user's consecutive commands satisfy the command sequence identification policy the user's consecutive commands become a command sequence. If the command sequence satisfies the compound command policy, the compound generation mechanism can generate a compound command for the command sequence automatically or prompt an administrator to allow the compound command to be generated. Generating a compound command can be done on a user by user basis or on a system wide basis. The compound command can then be displayed to the user to execute so that the command sequence is performed by the user selecting the compound command for execution.

Abstract: A computer system provides a way to automatically generate compound commands that perform tasks made up of multiple simple commands. A compound command generation mechanism monitors consecutive user commands and compares the consecutive commands a user has taken to a command sequence identification policy. If the user's consecutive commands satisfy the command sequence identification policy the user's consecutive commands become a command sequence. If the command sequence satisfies the compound command policy, the compound generation mechanism can generate a compound command for the command sequence automatically or prompt an administrator to allow the compound command to be generated. Generating a compound command can be done on a user by user basis or on a system wide basis. The compound command can then be displayed to the user to execute so that the command sequence is performed by the user selecting the compound command for execution.