Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908773885411328 |
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| author | Deng, Ou Cong, Ruichen Xu, Jianting Nishimura, Shoji Ogihara, Atsushi Jin, Qun |
| author_facet | Deng, Ou Cong, Ruichen Xu, Jianting Nishimura, Shoji Ogihara, Atsushi Jin, Qun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators Deng, Ou Cong, Ruichen Xu, Jianting Nishimura, Shoji Ogihara, Atsushi Jin, Qun Machine Learning Artificial Intelligence Symbolic Computation 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. |
| title | Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators |
| topic | Machine Learning Artificial Intelligence Symbolic Computation |
| url | https://arxiv.org/abs/2510.05178 |