Abstract: A system and method for using heterogeneous data from multiple healthcare information sources in a medical decision support system is disclosed. Each healthcare information system stores medical data using a different local schema. The medical decision support system provides responses to user queries. A query is received from a user that is generated in a standardized global schema. The query includes information from medical ontologies. Database queries are generated from the user queries that use the medical ontologies to generate constraints in the queries. The medical ontologies are also used to infer database queries. The generated query is translated into multiple queries for the multiple healthcare systems wherein each query is in the local schema of the healthcare information system that is being queried. Each database query is transmitted to one of the healthcare information systems based on the local schema of the particular query.

Abstract: A method and system for generation and validation of clinical reports with built-in automated measurement and decision support is provided. XML templates may be used for ensuring report data quality. The template data may include static, dynamic, calculated data, and others. The validation method may be based on formal logical constraint specifications. The constraint descriptions may include data types, cardinality, order, co-occurrence, Boolean logic, read-only data, regular expression patterns, etc. The method can be used to validate collected data on-the-fly based on constraint specifications without human interaction.

Abstract: A computer system for integrating medical information using the Unified Medical Language System (UMLS) as a taxonomy of medical terms includes a learner module for learning from training data concept classifiers for a source ontology and a target ontology, a mapper module for generating associations between source and target ontology classifiers using the UMLS, an annotator module for extracting knowledge from test data using source and target ontology classifiers and UMLS, and a feedback module for ranking associations according to a level or certainty, and presenting those associations that fall below a predefined level of certainty to a user for correction.

Abstract: A method for extracting an attribute occurrence from template generated semi-structured document comprising multi-attribute data records comprises identifying a first set of attribute occurrences in the template generated semi-structured document using an ontology. The method further comprises determining a boundary of each multi-attribute data record in the template generated semi-structured document, learning a pattern for an attribute corresponding to an identified attribute occurrence of the first set in the template generated semi-structured document, and applying the pattern within the boundary of each multi-attribute data record in the template generated semi-structured document to extract a second set of attribute occurrences.