AI for health will only scale when models are not only accurate but also readable, auditable, and governable. Many clinical and public-health decisions hinge on numeric thresholds -- cut-points that trigger alarms, treatment, or follow-up -- yet most machine-learning systems bury those thresholds inside opaque scores or smooth response curves. We introduce logistic-gated operators (LGO) for symbolic regression, which promote thresholds to first-class, unit-aware parameters inside equations and map them back to physical units for direct comparison with guidelines. On public ICU and population-health cohorts (MIMIC-IV ICU, eICU, NHANES), LGO recovers clinically plausible gates on MAP, lactate, GCS, SpO2, BMI, fasting glucose, and waist circumference while remaining competitive with established scoring systems (AutoScore) and explainable boosting machines (EBM). The gates are sparse and selective: they appear when regime switching is supported by the data and are pruned on predominantly smooth tasks, yielding compact formulas that clinicians can inspect, stress-test, and revise. As a standalone symbolic model or a safety overlay on black-box systems, LGO helps translate observational data into auditable, unit-aware rules for medicine and other threshold-driven domains.

翻译：只有当模型不仅准确，而且可读、可审计且可管控时，人工智能在健康领域的应用才能实现规模化。许多临床和公共卫生决策依赖于数值阈值——触发警报、治疗或随访的临界点——然而大多数机器学习系统将这些阈值隐藏在模糊的评分或平滑的响应曲线中。我们为符号回归引入了逻辑门控算子，它将阈值提升为方程中一等、单位感知的参数，并将其映射回物理单位，以便与指南进行直接比较。在公开的ICU和人群健康队列数据上，LGO恢复了在平均动脉压、乳酸、格拉斯哥昏迷评分、血氧饱和度、身体质量指数、空腹血糖和腰围等指标上具有临床合理性的门控，同时在与现有评分系统和可解释增强机模型的性能对比中保持竞争力。这些门控具有稀疏性和选择性：当数据支持状态切换时它们出现，而在以平滑趋势为主的任务中则被剪枝，从而产生临床医生可检查、压力测试和修订的紧凑公式。无论是作为独立的符号模型，还是作为黑盒系统的安全覆盖层，LGO都有助于将观测数据转化为适用于医学及其他阈值驱动领域的、可审计且单位感知的规则。