通过医学本体与认知映射的无训练临床推理：一种符号-概率知识图谱框架
Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
AI总结：
提出CKG Reasoner框架，通过医学本体和认知映射实现无训练临床推理，利用符号-概率知识图谱整合证据，在六个回顾性队列中取得高F1分数，验证了知识驱动诊断的可行性。
AI中文摘要：
大多数临床预测系统学习患者-变量-结局关联；我们研究一种无训练诊断范式，将患者观察映射到显式医学知识。CKG Reasoner整合了候选特异性证据特征节点、患者-参考匹配、有界信息门、知识加权证据累积、疾病相似性和决定性临床规则。缺失感知归一化和覆盖审计区分缺失证据与不可用证据。候选排序与不依赖结局标签的K-means聚类分离，该聚类使用四个派生证据坐标（证据强度、相对幅度、方向相似性和证据完整性），而非原始预测因子或目标，以推导队列级分配。在六个回顾性队列中——四个登革热队列（N = 1000, 1523, 989, 1018）、疟疾（N = 2190）和流感（N = 4569）——一个统一、无标签、队列拟合的K = 2方案产生了阳性类F1分数分别为0.996、0.634、0.936、0.917、0.695和0.842，以及全记录准确率分别为0.996、0.558、0.914、0.893、0.707和0.906，使用冻结包和疾病特定知识表示实现了完全分区决策覆盖。评分和聚类均不使用结局标签。逻辑回归提供监督基线。流感包含确认性分子PCR，并非独立的检测前预测。结果表征了知识基础的证据分离、可审计性和敏感性，而非前瞻性临床有效性或比较优越性。基于FOL/LLM的临床解释尚未评估。
英文摘要：
Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit medical knowledge. CKG Reasoner integrates candidate-specific Evidence Feature Nodes, patient-reference matching, a bounded Information Gate, knowledge-weighted evidence accumulation, disease similarity, and decisive clinical rules. Missing-aware normalization and coverage auditing distinguish absent from unavailable evidence. Candidate ranking is separate from outcome-label-independent K-means clustering, which uses four derived evidence coordinates (evidence strength, relative magnitude, directional similarity, and evidence completeness), not raw predictors or targets, to derive cohort-level assignments. Across six retrospective cohorts - four dengue (N = 1000, 1523, 989, 1018), malaria (N = 2190), and influenza (N = 4569) - a uniform, label-free, cohort-fitted K = 2 protocol yielded positive-class F1 scores of 0.996, 0.634, 0.936, 0.917, 0.695, and 0.842, and all-record accuracies of 0.996, 0.558, 0.914, 0.893, 0.707, and 0.906, respectively, with full partition-decision coverage using the frozen package and disease-specific knowledge representations. Neither scoring nor clustering uses outcome labels. Logistic regression provides a supervised baseline. Influenza incorporates confirmatory molecular PCR and is not independent pre-test prediction. Results characterize knowledge-grounded evidence separation, auditability, and sensitivity, not prospective clinical validity or comparative superiority. FOL/LLM-based clinical explanation remains unevaluated.
