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Related Experiment Video

Updated: Jan 14, 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

1.3K

A hybrid decision support system using rule-based and AI methods: the OnCATs knowledge-based framework.

Nuno Soares Domingues1

  • 1Instituto Politécnico de Lisboa/Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emidio Navarro, 1, 1959-007 Lisbon, Portugal.

International Journal of Medical Informatics
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

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This study developed OnCATs, an explainable clinical decision support system (CDSS) for prostate cancer, which successfully reproduced guideline-based decisions using transparent, rule-based reasoning.

Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Clinical decision support systems (CDSS) often use opaque AI, hindering transparency and reproducibility in oncology.
  • Prostate cancer care requires adaptable decision support due to complex factors like tumor stage, PSA, Gleason score, and comorbidities.
  • There is a need to bridge explainability and adaptability in CDSS for trustworthy prostate cancer management.

Purpose of the Study:

  • To develop and evaluate OnCATs, a novel modular and explainable CDSS for prostate cancer.
  • To encode international prostate cancer management guidelines into a machine-readable and auditable format.
  • To create a hybrid-ready CDSS capable of integrating future advancements.

Main Methods:

  • Formalized evidence from 23 international guidelines into a JSON-based rule base.

Related Experiment Videos

Last Updated: Jan 14, 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

1.3K
  • Utilized a forward-chaining inference engine to execute the rules within OnCATs.
  • Implemented three decision layers: risk stratification, treatment-pathway recommendation, and prescription-level assistance.
  • Assessed feasibility and performance using ten published case reports and standard metrics (precision, recall, F1).

    • Main Results:

    • OnCATs achieved perfect concordance (F1=1.00) for risk stratification.
    • Treatment-pathway recommendation concordance reached an F1 score of 0.80.
    • Prescription-level assistance showed agreement ranging from 0.67 to 0.75 (mean F1=0.71).
    • Observed divergences were attributed to simplified life-expectancy modeling and incomplete case data.

    Conclusions:

    • OnCATs successfully demonstrated transparent, rule-based reasoning for guideline-defined prostate cancer decisions with traceability.
    • The system operationalizes multi-source guidelines into an explainable and modular CDSS.
    • OnCATs provides a reproducible foundation for integrating advanced AI and machine learning methods in prostate cancer care.