An update to experimental models for validating computer technology

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Experimentation is important within science for determining the effectiveness of proposed theories and methods.However, computer science has not developed a concise taxonomy of methods applicable for demonstrating the validity of a new technique.This has been successfully applied, for example, in the GO (Gene Data model) project which provides a taxonomy of concepts and their attributes for annotating gene products.Similar projects include the Mouse Gene Database (MGD) and the Mouse Gene Expression Database (GXD).For example, Expert Health Data Programming provides the Vitalnet software for linking and disseminating health data sets; CCS Informatics provides the e Loader software which automates loading data into ORACLE® Clinical; PPD Patient Profiles enables visualization of key patient data from clinical trials; and TABLETRANS® enables specification of data transformations graphically.Depending on the tool, automated approaches to data integration can be far less resource intensive than the manual data integration, but will always be more constrained.Data is converted into standardized data classes using a data parser specifically tailored to the source system. 7, 2004, the disclosure of which is incorporated by reference herein in its entirety. Field of the Invention The invention relates generally to the field of analyzing, managing and acting upon clinical information, and specifically to a system which integrates genetic and phenotypic data from a group of subjects into a standardized format in order to validate the data and to make better decisions related to the genetic and phenotypic information of a particular subject. Description of the Related Art The current methods by which clinical decisions are made do not make the best possible use of existing information management technologies.

The information management system disclosed enables caregivers to make better decisions by using aggregated data.

However, no system exists today to combine all phenotypic and genetic information associated with a patient into a single data model; to create a series of logical and statistical interrelationships between the data classes of that standard; to continually upgrade those relationships based on the data from multiple subjects and from different databases; and to use that information to make better decisions for an individual subject.

Prior art exists to manage information in support of caregivers and for streamlining clinical trials.

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