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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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Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation.

Francesco De Pretis1,2, Jürgen Landes3, William Peden4,5

  • 1Department of Biomedical Sciences and Public Health, School of Medicine and Surgery, Marche Polytechnic University, Ancona, Italy.

Journal of Evaluation in Clinical Practice
|February 11, 2021
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) enhances Bayesian drug safety assessments by improving information retrieval and data analysis. This integration allows for more robust evaluation of adverse reaction probabilities from diverse evidence types.

Keywords:
E-Synthesisartificial intelligencedrug safetyevidence evaluationpharmacosurveillancepharmacovigilance

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Area of Science:

  • Pharmacovigilance
  • Computational toxicology
  • Evidence-based medicine

Background:

  • Assessing drug safety profiles is challenging due to diverse evidence types (case reports, animal/observational studies).
  • Frequentist inference methods struggle to aggregate these varied signals effectively.
  • Bayesian approaches offer a more flexible framework for synthesizing evidence in drug safety assessments.

Purpose of the Study:

  • To explore the synergistic potential of Artificial Intelligence (AI) within the E-Synthesis Bayesian framework for drug safety evaluations.
  • To enhance the aggregation of diverse evidence for more accurate drug safety profile assessments.
  • To facilitate the practical application of theoretical principles in drug safety evidence aggregation.

Main Methods:

  • E-Synthesis, a Bayesian framework, aggregates all available information to calculate the probability of a drug causing an adverse reaction.
  • Development of AI systems for automated evidence aggregation in medicine.
  • Integration of AI for information retrieval, decision support, and probability learning within the E-Synthesis framework.

Main Results:

  • AI significantly aids E-Synthesis in information retrieval, usability via graphical aids, and learning Bayes factors from historical data.
  • AI assists in assessing information quality and determining conditional probabilities for E-Synthesis's 'indicators' of causation.
  • E-Synthesis provides a robust methodological foundation for AI-driven (semi-)automated evidence aggregation.

Conclusions:

  • AI integration can bridge the gap between theoretical principles of evidence aggregation and practical drug safety tools.
  • The combination of AI and E-Synthesis promises more efficient and reliable drug safety assessments.
  • Proper application of AI can advance the field of pharmacovigilance through enhanced data synthesis.