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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

Retno Vinarti1, Lucy Hederman1

  • 1School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Ireland.

Studies in Health Technology and Informatics
|April 22, 2018
PubMed
Summary

This study introduces a knowledge base for infectious disease risk (IDR) that auto-generates Bayesian Networks for personalized risk prediction. The system successfully models IDR for individual-level health assessments.

Keywords:
infectious diseaseknowledge-baseontologyriskrules

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Area of Science:

  • Computational epidemiology
  • Medical informatics
  • Knowledge representation

Background:

  • Infectious disease risk (IDR) prediction requires structured knowledge for automated analysis.
  • Existing knowledge sources are often declarative and not directly usable for probabilistic modeling.
  • Personalized risk assessment necessitates a system that can process individual and contextual factors.

Purpose of the Study:

  • To develop a knowledge-base for representing infectious disease risk (IDR) knowledge.
  • To create an algorithm capable of auto-generating Bayesian Networks (BNs) from this knowledge-base.
  • To evaluate the knowledge-base's suitability for a personalized IDR prediction system.

Main Methods:

  • Compiled knowledge from 234 infectious diseases from declarative sources.
  • Designed a general ontology and five rule types for modeling IDR.
  • Developed an algorithm to auto-generate Bayesian Networks (BNs).
  • Evaluated the knowledge-base structure and its application in three disease-country contexts.

Main Results:

  • The knowledge-base successfully integrated information from a large number of infectious diseases.
  • The developed ontology and rules provide a generalizable framework for IDR knowledge.
  • The auto-generation of BNs from the knowledge-base was demonstrated.
  • Evaluation confirmed the knowledge-base's effectiveness in supporting personalized IDR prediction.

Conclusions:

  • The presented knowledge-base effectively structures infectious disease risk information.
  • The system enables automated generation of Bayesian Networks for risk prediction.
  • The knowledge-base meets the requirements for a personalized infectious disease risk prediction system.