SMARtCARE：面向有界自主临床决策支持的隐私保护智能体AI系统
SMARtCARE: Privacy-Preserving Agentic AI Systems for Bounded-Autonomy Clinical Decision Support
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中文总结 AI 辅助
SMARtCARE提出四状态临床决策支持架构，利用有损六通道指纹识别中间上下文风险，通过元认知升级和患者身份防护实现隐私保护，并在MIMIC数据集上验证了可追溯性。
中文摘要 AI 辅助
长上下文临床AI系统在既往入院记录超出活跃推理上下文时，可能会遗漏相关的患者病史。在ICU监测中，这可能导致早期生命体征漂移即使与既往恶化模式相似，也会显得非特异性。SMARtCARE通过一个四状态临床决策支持架构来解决这一缺口：稳定（Stable）、元认知（Meta-cognitive）、辅助（Assisted）和监管（Regulated，即撤销）。SMARtCARE并非自动检索既往记录，而是使用患者既往轨迹的有损六通道指纹。当当前漂移与该指纹匹配且既往记录不在上下文中时，系统会触发元认知升级以供临床医生审查；完整检索仅在辅助状态下通过临床医生的操作进行。系统设计了一个患者身份防护机制，以确保在数据加载、日志记录和审计层中正确归属。评估结合了合成蒙特卡洛研究（用于验证状态转换逻辑和估计器稳定性，而非临床性能）以及在MIMIC-III和MIMIC-IV临床数据库演示上的真实数据运行。在MIMIC-III上，14名两次入院患者中识别出1例既往模式复发；在MIMIC-IV上，相同流程在9名两次入院患者中未产生指纹匹配，这说明了固定规范模式库的一个关键局限性。在两次运行中，所有记录的决策均可完全追溯且归属正确。结果支持SMARtCARE作为揭示中间上下文风险的、可追溯且隐私感知的机制；这并非临床有效性声明。
英文摘要
Long-context clinical AI systems can miss relevant patient history when prior admissions fall outside the active reasoning context. In ICU monitoring, this can cause early vital-sign drift to appear nonspecific even when it resembles a prior deterioration pattern. SMARtCARE addresses this gap through a four-state clinical decision-support architecture: Stable, Meta-cognitive, Assisted, and Regulated (Revoked). Rather than automatically retrieving prior records, SMARtCARE uses a lossy six-channel fingerprint of the patient's prior trajectory. When current drift matches that fingerprint and the prior record is absent from context, the system raises a Meta-cognitive escalation for clinician review; full retrieval occurs only through clinician action in the Assisted state. A patient-identity guard is designed to enforce correct attribution across data loading, logging, and audit layers. Evaluation combines a synthetic Monte Carlo study that validates the state-transition logic and estimator stability, not clinical performance, with real-data runs on both the MIMIC-III and MIMIC-IV Clinical Database Demos. On MIMIC-III, one prior-pattern recurrence was identified among 14 two-admission patients; on MIMIC-IV, the same pipeline produced no fingerprint matches among 9 two-admission patients, which illustrates a key limitation of a fixed canonical pattern library. Across both runs all logged decisions were fully traceable and correctly attributed. The results support SMARtCARE as a traceable, privacy-aware mechanism for surfacing middle-context risk; they are not a clinical efficacy claim.
发表机构
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
