Soares Domingues, Nuno Alexandre2026-05-042026-05-042026-02Domingues, N. S. (2026). A hybrid decision support system using rule-based and AI methods: the OnCATs knowledge-based framework. International Journal of Medical Informatics, 206, 1-11. https://doi.org/10.1016/j.ijmedinf.2025.1061441386-5056http://hdl.handle.net/10400.21/22849Background and significance: Clinical decision support systems (CDSS) can improve evidence-based oncology care, but many rely on opaque AI models that limit transparency and reproducibility. Rule-based approaches provide interpretability but often lack adaptability, a critical issue in prostate cancer where decisions depend on tumor stage, PSA, Gleason score, comorbidities, and life expectancy. Bridging explainability and adaptability is essential for trustworthy decision support. Objective: To develop and evaluate OnCATs, a modular, explainable, hybrid-ready CDSS that encodes prostate cancer management guidelines in a machine-readable and auditable format. Materials and methods: Evidence from 23 international guidelines was formalized into a JSON-based rule base executed through a forward-chaining inference engine. OnCATs supports three decision layers: (1) risk stratification, (2) treatment-pathway recommendation, and (3) prescription-level assistance for radiotherapy, brachytherapy, androgen deprivation therapy, and surgery. Feasibility was tested using ten published case reports. Performance was assessed with precision, recall, F1 scores, and descriptive concordance. Results: OnCATs achieved perfect concordance for risk stratification (precision = 1.00, recall = 1.00, F1 = 1.00). Treatment-pathway concordance was 0.80 (F1 = 0.80). Prescription-level agreement ranged from 0.67 to 0.75 (mean F1 = 0.71). Divergences primarily reflected simplified life-expectancy modeling and incomplete case data. Discussion: OnCATs demonstrates that transparent, rule-based reasoning can reproduce guideline-defined prostate cancer decisions with traceability. Limitations include the small sample size and reliance on secondary data. Conclusion: OnCATs operationalizes multi-source guidelines into an explainable, modular CDSS, providing a reproducible foundation for future integration of probabilistic and machine-learning methods.engHybrid decisionAI methodsOnCATs knowledgeresearch article10.1016/j.ijmedinf.2025.1061441872-8243