用可微结构学习发现临床时间序列的因果关系，提升预测准确性和可解释性。
Differentiable latent structure discovery for interpretable forecasting in clinical time series
- 通过可微图学习和过程卷积，自动挖掘变量间的稀疏有向依赖关系。
- 在1008例脓毒症患者数据上，6小时预测误差降低34%，长程预测覆盖率达93%。
- 适合关注医疗决策可解释性、模型可信度的研究者和临床工程师。
背景：我们提出 StructGP，一种连续时间多任务高斯过程，结合过程卷积与可微结构学习，以发现稀疏有序的有向无环图（DAG）式变量依赖关系，同时保持合理的不确定性建模。进一步提出 LP-StructGP，通过共享的潜路径——由受试者特异性耦合滤波器和Softmax门控机制推断出的时间平移轨迹——捕捉跨患者的进展模式。两个模型均在稀疏性和无环性约束下，使用基于似然的可扩展低秩更新进行训练。结果：模拟实验中，图恢复随队列规模增加而改善，最大队列规模下中位结构汉明距离为零，路径分配的调整兰德指数较高。分析表明，有序 StructGP 图可从群体边际似然中识别。在 MIMIC-IV 脓毒症队列（n=1,008；去甲肾上腺素、肌酐、平均血压）上，StructGP 在短时（6小时）预测上优于独立任务基线（平均RMSE 0.68 [95% CI: 0.63-0.74] vs. 0.88 [0.83-0.94]），且在增加15个输入后显著超越无结构核（0.63 [0.58-0.69] vs. 3.02 [2.85-3.18]），校准性能更优（覆盖率0.96 vs. 0.84）。在长时程（长达6天）预测中，LP-StructGP 进一步降低肌酐误差（RMSE 0.95 [0.88-1.03] vs. 1.17 [1.08-1.25]），并提升整体覆盖率（0.93 [0.93-0.94] vs. 0.91 [0.91-0.92]）。在 PhysioNet 挑战赛中，StructGP 达到竞争性精度（MAE 3.72e-2），优于强基准图神经网络模型。结论：结构化过程卷积结合潜路径，实现了对不规则临床时间序列的可解释、可扩展且校准良好的预测。
原文摘要 · Abstract (English)
Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty. We further propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns. Both models are trained under sparsity and acyclicity constraints using scalable low-rank updates using likelihood-based objectives. Results: In simulations, graph recovery improved with cohort size, with the median Structural Hamming Distance reaching zero at the largest cohort size, while pathway assignments showed high Adjusted Rand Index. Our analysis establishes that the ordered StructGP graph is identifiable from the population marginal likelihood. On a MIMIC-IV septic shock cohort (n=1,008; norepinephrine, creatinine, mean blood pressure), StructGP improves short-horizon (6 h) forecasting over independent-task baselines (average RMSE 0.68 [95% CI: 0.63-0.74] vs. 0.88 [0.83-0.94]) and, with 15 additional inputs, markedly outperforms unstructured kernels (0.63 [0.58-0.69] vs. 3.02 [2.85-3.18]) with superior calibration (coverage 0.96 vs. 0.84). For long horizons (up to 6 days), LP-StructGP further reduces error for creatinine (RMSE 0.95 [0.88-1.03] vs. 1.17 [1.08-1.25]) and improves overall coverage (0.93 [0.93-0.94] vs. 0.91 [0.91-0.92]). On the PhysioNet Challenge, StructGP attains competitive accuracy (MAE 3.72e-2) relative to a strong published graph neural model. Conclusion: These results show that structured process convolutions with latent pathways deliver interpretable, scalable, and well-calibrated forecasting for irregular clinical time series.
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