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Guiding principles for technical infrastructure to support computable biomedical knowledge.

Jamie McCusker1, Leslie D McIntosh2,3, Chris Shaffer4

  • 1Rensselaer Polytechnic Institute Computer Science Troy New York USA.

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Summary
This summary is machine-generated.

This study outlines key principles for developing computable biomedical knowledge (CBK) infrastructure. It emphasizes interoperability, trustworthy representations, and open standards for advancing healthcare knowledge accessibility and usability.

Keywords:
FAIRcomputable biomedical knowledgeopen systems

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Area of Science:

  • Biomedical Informatics
  • Health Systems Science
  • Knowledge Management

Background:

  • The Mobilizing Computable Biomedical Knowledge working group identified the need for robust infrastructure to utilize computable biomedical knowledge (CBK).
  • Existing systems often struggle to bridge the gap between raw data and actionable, computable knowledge.
  • The integration of Learning Health Systems and FAIR principles is crucial for effective knowledge translation.

Purpose of the Study:

  • To conceptualize and lay the foundation for essential CBK infrastructure.
  • To differentiate between computable knowledge and data.
  • To propose guiding principles for the advancement of CBK infrastructure development.

Main Methods:

  • Conceptualization and summarization of the working group's findings.
  • Contextualization within Learning Health Systems and FAIR principles.
  • Formulation of three core principles for CBK infrastructure development.

Main Results:

  • Defined the distinction between computable knowledge and data.
  • Contextualized CBK infrastructure within broader healthcare and data management frameworks.
  • Proposed three guiding principles: interoperability, stable/trustworthy representations, and openness.

Conclusions:

  • Advancing CBK infrastructure requires promoting interoperable systems for findable, accessible, interoperable, and reusable (FAIR) data and knowledge.
  • Stable, trustworthy, human- and machine-readable knowledge representations are essential.
  • Openness of computable knowledge resources and supporting standards is critical for widespread adoption and impact.