用规则强化学习修正医疗模型的生理不合理预测
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer
- 通过规则驱动的强化学习层修正模型输出，确保生理合理性
- 在多个医疗分类任务中显著降低不合理预测，提升准确率
- 无需复杂专家知识，仅需少量不可能状态规则即可生效
本文提出一种新型医疗健康领域强化学习范式：为任意预测模型增加规则化强化学习层（RRLL），以修正其生理上不可能的预测结果。RRLL接收预测标签作为状态，输出校正后的标签作为动作，状态-动作对的奖励由一组通用规则评估。该方法高效、通用且轻量，不依赖复杂专家知识，仅需一组不可能转移状态，其数量远小于所有可能转移；但能有效减少当前先进预测模型产生的生理不合理错误。我们在多种重要医疗分类问题上验证了RRLL的有效性，相同设置下均取得显著改进，仅需更换领域特定的不可能规则集。深入分析表明，RRLL通过有效减少生理不合理预测提升了模型准确性。
原文摘要 · Abstract (English)
This paper adds to the growing literature of reinforcement learning (RL) for healthcare by proposing a novel paradigm: augmenting any predictor with Rule-based RL Layer (RRLL) that corrects the model's physiologically impossible predictions. Specifically, RRLL takes as input states predicted labels and outputs corrected labels as actions. The reward of the state-action pair is evaluated by a set of general rules. RRLL is efficient, general and lightweight: it does not require heavy expert knowledge like prior work but only a set of impossible transitions. This set is much smaller than all possible transitions; yet it can effectively reduce physiologically impossible mistakes made by the state-of-the-art predictor models. We verify the utility of RRLL on a variety of important healthcare classification problems and observe significant improvements using the same setup, with only the domain-specific set of impossibility changed. In-depth analysis shows that RRLL indeed improves accuracy by effectively reducing the presence of physiologically impossible predictions.
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