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Related Experiment Video

Updated: Jun 28, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Versioning computer-interpretable guidelines: semi-automatic modeling of 'Living Guidelines' using an information

Katharina Kaiser1, Silvia Miksch

  • 1Vienna University of Technology, Institute of Software Technology & Interactive Systems, Favoritenstrasse 9-11/188, A-1040 Vienna, Austria. kaiser@ifs.tuwien.ac.at

Artificial Intelligence in Medicine
|October 28, 2008
PubMed
Summary
This summary is machine-generated.

Automating the formalization of clinical practice guidelines (CPGs) with the LASSIE method speeds up the creation of living guidelines. This ensures healthcare professionals receive the most current, evidence-based medical knowledge efficiently.

Related Experiment Videos

Last Updated: Jun 28, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Health Informatics
  • Medical Informatics
  • Knowledge Engineering

Background:

  • Clinical practice guidelines (CPGs) require continuous updates to provide current, evidence-based medical knowledge.
  • Living guidelines offer a solution for delivering up-to-date information but require formalization for computer interpretability.
  • Formalizing guidelines is complex and time-consuming, necessitating automated solutions for efficient updates.

Purpose of the Study:

  • To present a time-saving and effective method for formalizing updated versions of previously formalized CPGs.
  • To demonstrate the automation of modeling processes for guideline updates.
  • To facilitate the implementation of living guidelines for healthcare professionals.

Main Methods:

  • The LASSIE methodology formalizes guidelines from textual form to the Asbru language using a document-centric approach.
  • Information extraction techniques are employed to semi-automatically formalize guideline components.
  • LASSIE supports the implementation of living guidelines by streamlining the update process.

Main Results:

  • Adaptations of previously formalized guidelines can be accomplished easily and rapidly.
  • Unchanged models are inherited from previous versions due to traceable formalization, minimizing redundant work.
  • Only updated text parts require formalization using the LASSIE method, significantly reducing effort.

Conclusions:

  • The LASSIE method offers a simple, time-saving, and effective approach to formalize new guideline versions.
  • Previously formalized models, whether added or modified, can be easily transferred.
  • Faster implementation of living guidelines ensures healthcare professionals have access to up-to-date knowledge.