AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines
Not on the map. Why?
Abstract (arXiv)
Bladder cancer claims one life every 3 minutes worldwide. Most patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC), yet up to 70% recur after treatment, triggering a relentless cycle of surgeries, monitoring, and risk of progression. Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases. We propose an interpretable AI model using the Tsetlin Machine (TM), a symbolic learner that outputs transparent, human-readable logic. Tested on the PHOTO trial dataset (n=330), TM achieved an F1-score of 0.80, outperforming XGBoost (0.78), Logistic Regression (0.60), and EORTC (0.42). TM reveals the exact clauses behind each prediction, grounded in clinical features like tumour count, surgeon experience, and hospital stay - offering accuracy and full transparency. This makes TM a powerful, trustworthy decision-support tool ready for real-world adoption.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 19 references that are in the map. First posted on arXiv 26 Jul 2025.
Builds on
5 of 19 references.
References · 5
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
- 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 72
- 2018Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applicationscited by 24
- 2023Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identificationcited by 3
- 2025Attention-enabled Explainable AI for Bladder Cancer Recurrence Predictioncited by 2
Built on by
Not cited yet.