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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Nine Principles of Semantic Harmonization.

James A Cunningham1, Michel Van Speybroeck2, Dipak Kalra3

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Summary
This summary is machine-generated.

Semantic harmonization unifies diverse medical data into a consistent view. Nine guiding principles were developed to create robust frameworks for collating complex cohort data, like that from Alzheimer's disease research.

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Area of Science:

  • Health Informatics
  • Data Science
  • Biomedical Research

Background:

  • Medical data is collected across institutions in various formats.
  • Semantic harmonization aims to create a unified, consistent data view.
  • Existing harmonization approaches often result in bespoke, ad-hoc systems, especially for cohort-specific data.

Purpose of the Study:

  • To develop a set of generic guiding principles for creating semantic harmonization frameworks.
  • To provide a foundation for constructing robust semantic harmonization systems for diverse medical data.
  • To address the challenges of harmonizing cohort data, exemplified by pan-European Alzheimer's data.

Main Methods:

  • Development of the 'EMIF Knowledge Object Library' as a semantic harmonization framework.
  • Guidance from practical experience in collating pan-European Alzheimer's cohort data.
  • Formulation of nine generic guiding principles for framework development.

Main Results:

  • A set of nine generic guiding principles for developing semantic harmonization frameworks.
  • A demonstrated application of these principles through the EMIF Knowledge Object Library.
  • Establishment of a solid base for constructing future semantic harmonization frameworks.

Conclusions:

  • The proposed guiding principles offer a structured approach to semantic harmonization.
  • These principles facilitate the creation of reusable and adaptable harmonization frameworks.
  • Implementing these principles can improve the consistency and utility of medical data across different sources.