An update to experimental models for validating computer technology

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.

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.

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None of these systems make use of expert and statistical relationships between data classes in a standardized data model in order to validate data or make predictions; or provide a mechanism by which electronically published rules and statistical models can be automatically input for validating data or making predictions; or guarantee strict compliance with data privacy standards by verifying the identity of the person accessing the data with biometric authentication; or associate all clinical data with a validator the performance of which is monitored so that the reliability of data from each independent source can be efficiently monitored; or allow for compensation of individuals for the use of their data; or allow for compensation of validators for the validation of that data.Bayesian classification schemes such as MAGIC (Multisource Association of Genes by Integration of Clusters) have been created to integrate information from multiple sources into a single normative framework, using expert knowledge about the reliability of each source.Several commercial enterprises are also working on techniques to leverage information across different platforms.For example, success has been achieved with tools that input textual data and generate standardized terminology in order to achieve information integration such as, for example, the Unified Medical Language System (UMLS): Integration Biomedical Terminology.Tools have been developed to inhale data into new ontologies from specific legacy systems, using object definitions and Extensible Markup Language (XML) to interface between the data model and the data source, and to validate the integrity of the data inhaled into the new data model.

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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.

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