arXiv：cs.LG· Jin-Jia Zheng, Vincent S. Tseng·· 4 小时前
面向纵向电子健康记录疾病轨迹的两级临床新颖性优先级框架
A Two-Level Clinical Novelty Prioritization Framework for Disease Trajectories Discovered from Longitudinal Electronic Health Records
AI 导读
研究提出一个两级框架，对上游基于 MIMIC-IV v3.1 发现的 99,527 个分支 motif 进行语义新颖性优先级排序，将路径级与节点到终点语义稀疏度同可靠性校正后的临床优先级分离，并把每个 motif 的三种确定性文本化用 BAAI/bge-small-en-v1.5 嵌入、对照 166,972 篇 2023 年前 PubMed 文献校准。
正文
Abstract:Graph-based analysis of electronic health records (EHRs) yields disease-trajectory catalogs, but structural support does not establish semantic novelty. We present a two-level framework to prioritize 99,527 branching motifs previously discovered by an upstream patient-disjoint, train-only MIMIC-IV v3.1 pipeline and frozen after validation-based catalog selection. The framework separates path-level and node-to-endpoint semantic sparsity from reliability-adjusted clinical priority. Three deterministic textualizations per motif or node-target pair were embedded with BAAI/bge-small-en-v1.5 and calibrated against 166,972 pre-2023 PubMed references using mean top-20 cosine distance. Positive recombination occurred in 8.63% of motifs and had negligible effect on global ranking, whereas specificity and support/GNN reliability terms changed clinical-priority ordering. Robustness analyses across neighbor counts, templates, encoders, generic-label taxonomies, score settings, PrimeKG, and corrected density-based RND showed that broad ordering was more stable than hard top-k membership. A semantic-dose analysis did not support a monotonic relation between score and forecasting benefit after endpoint and token-count adjustment (1.96 days per SD; 95% CI, -0.75 to 4.68). In the frozen catalog-q90 high-semantic subgroup, branch-enhanced forecasting reduced conditional event-time MAE by 20.1 days (95% CI, 13.3-27.0) and sMAPE by 2.37 percentage points (95% CI, 1.81-2.93), while discrimination and endpoint-ranking differences were inconclusive. Corrected RND showed low tail agreement with the primary score (Spearman rho = 0.430; q90 Jaccard = 0.125) despite a similar aggregate timing association. These findings support representation-aware, uncertainty-aware literature-relative prioritization of discovered trajectories rather than causal discovery or clinician-adjudicated novelty.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20512 [cs.LG] |
| (or arXiv:2610.20512v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20512 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vincent S. Tseng [view email]
[v1]
Wed, 14 Oct 2026 17:14:29 UTC (921 KB)
来源：arXiv：cs.LG · arxiv.org