Volume 3,Issue 7
HealSQL：一个通过类强化学习范式自我优化的医疗
领域Text-to-SQL 系统
随着医疗信息化加速，海量数据为临床研究提供了巨大机遇，但非技术专家面临着复杂的数据查询鸿沟。为解决此问题，Text-to-SQL 技术应运而生，但在处理专业医疗术语和复杂临床逻辑时，其准确性与鲁棒性仍面临严峻挑战。本文提出并实现了一个名为“HealSQL”的 Text-to-SQL 系统，其核心愿景是赋予数据库系统以“自我诊断与愈合”的能力。首先，本研究构建了一个彻底解耦的、由外部知识库驱动的架构，将业务逻辑与通用查询引擎分离。其次，最关键的创新是设计了一个基于类强化学习（Reinforcement Learning-like）范式的自动化自愈闭环。在该闭环中，AI 测试代理模拟“病原体”生成对抗性问题，测试套件作为“诊断器”捕捉系统缺陷，AI 优化器则针对失败案例“对症下药”，生成知识库“补丁”以修复认知漏洞。这种方法使系统能够持续从错误中“康复”并建立对类似错误的“免疫机制”。实验证明，经过几轮自动化进化，系统在处理复杂临床查询上的准确率显著提升，验证了该“自愈”框架的有效性。
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