用可解释的AI模型预测膀胱癌复发，准确率超传统方法
AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines
- 采用符号学习的Tsetlin机模型，输出可读逻辑规则
- 在330例数据上F1达0.80，优于XGBoost和临床风险表
- 适合需要透明决策支持的医疗场景，医生可理解预测依据
膀胱癌每3分钟夺走一条生命。多数患者确诊为非肌层浸润性膀胱癌（NMIBC），但治疗后高达70%复发，导致反复手术与监测。现有临床工具如EORTC风险表已过时，尤其对中危患者不可靠。本文提出基于可解释AI的Tsetlin Machine（TM）模型，该模型输出人类可读的逻辑规则。在PHOTO试验数据集（n=330）上，TM实现F1分数0.80，优于XGBoost（0.78）、逻辑回归（0.60）和EORTC（0.42）。TM揭示了每个预测背后的临床特征逻辑，如肿瘤数量、术者经验、住院天数等，兼具高准确率与完全透明性，是可直接应用于临床的可信决策辅助工具。
原文摘要 · Abstract (English)
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 Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases.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.
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