Differentiable latent structure discovery for interpretable forecasting in clinical time series

• URL: http://arxiv.org/abs/2604.27967v1
• Date: Thu, 30 Apr 2026 14:59:50 GMT
• Title: Differentiable latent structure discovery for interpretable forecasting in clinical time series
• Abstract summary: We introduce StructGP, a continuous-time multi-task Gaussian process that couples differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.<n>We 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.
• Score: 38.473526928012724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approaches either impute to a grid or sacrifice interpretability. 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 (augmented Lagrangian, Adam) using scalable low-rank updates. Results: In simulations, the approach reliably recovers ground-truth graphs (Structural Hamming Distance approaching 0 as cohorts grow) and pathway assignments (high Adjusted Rand Index). On a MIMIC-IV septic shock cohort (n=1,008; norepinephrine, creatinine, mean arterial 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). On the PhysioNet Challenge (12k patients, 41 variables), StructGP attains competitive accuracy (MAE 3.72e-2) relative to a state-of-the-art graph neural model while maintaining calibrated uncertainty. Conclusion: These results show that structured process convolutions with latent pathways deliver interpretable, scalable, and well-calibrated forecasting for irregular clinical time series.

Related papers

• Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting [1.1240669509034296]
  This work investigates whether inferred behavioural structure can be embedded within a Neural Process-based probabilistic model.<n>We propose a behaviour-conditioned Attentive Neural Process framework that treats each load profile as a forecasting task.<n>Experiments on the Smart Grid, Smart City dataset use user-disjoint train/validation/test splits, variable context lengths, and multi-step forecast horizons.
  arXiv  Detail & Related papers  (2026-07-17T17:48:22Z)
• SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation [42.644852156101685]
  Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread.<n>We propose textbfSL-BiLEM (Structured Learnable Behavior-in-the-Loop Epidemic Model), leveraging physical constraints as regularization for robust extrapolation.
  arXiv  Detail & Related papers  (2026-05-26T08:46:04Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data [1.995753801570114]
  We propose an action-conditioned latent world-model framework for learning patient trajectories from longitudinal care data.<n>We instantiate CMWM for annual estimated glomerular filtration rate (eGFR) trajectory forecasting in chronic kidney disease (CKD)<n>The framework is not CKD-specific its architecture, loss design, and training protocol apply to any chronic condition that can be cast as periodic clinical state interleaved with structured and conversational interventions.
  arXiv  Detail & Related papers  (2026-05-21T03:50:17Z)
• Rethinking Molecular OOD Generalization via Target-Aware Source Selection [10.102858221967791]
  SCOPE-BENCH is a benchmark built on cluster-level partitioning in an explicit physicochemical descriptor space.<n>POMA is a framework that formulates knowledge transfer as a retrieve-compose-adapt pipeline.<n> Evaluations show that prediction errors of state-of-the-art 3D molecular models surge by up to 8.0x on SCOPE-BENCH with a mean of 5.9x.
  arXiv  Detail & Related papers  (2026-05-13T16:09:46Z)
• Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction [3.0602131532925436]
  Recent methods have moved toward increasing generative complexity, but the conditions under which this complexity is necessary remain unclear.<n>We propose that generative complexity should match the entropy of a predictable task's conditional posterior component, with training-inference input alignment required in all regimes.<n>We validated this framework on a fundus autofluorescence dataset by contrasting five configurations.
  arXiv  Detail & Related papers  (2026-04-18T10:28:00Z)
• Diagnosing Heteroskedasticity and Resolving Multicollinearity Paradoxes in Physicochemical Property Prediction [0.2330146832085843]
  We analyzed 426,850 bioactive molecules from PubChem, ChEMBL, and eMolecules databases.<n>We found severe heteroskedasticity in linear models predicting computed logP values.<n>Tree-based ensemble methods proved inherently robust to heteroskedasticity while delivering superior predictive performance.
  arXiv  Detail & Related papers  (2025-12-31T05:32:13Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• Unsupervised Cycle Detection in Agentic Applications [7.001329254828447]
  Agentic applications powered by Large Language Models exhibit non-deterministic behaviors that can form hidden execution cycles.<n>Traditional observability platforms fail to detect these costly inefficiencies.<n>We present an unsupervised cycle detection framework that combines structural and semantic analysis.
  arXiv  Detail & Related papers  (2025-10-31T13:27:53Z)
• AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data [46.262619407930266]
  We present AttentiveGRUAE, a novel attention-based gated recurrent unit (GRU) autoencoder designed for temporal clustering and prediction of outcome from longitudinal wearable data.<n>We evaluate AttentiveGRUAE on longitudinal sleep data from 372 participants (GLOBEM 2018-2019)<n>It demonstrates superior performance over baseline clustering, domain-aligned self-supervised, and ablated models in both clustering quality and depression classification.
  arXiv  Detail & Related papers  (2025-10-02T20:52:16Z)
• Enhancing ALS Progression Tracking with Semi-Supervised ALSFRS-R Scores Estimated from Ambient Home Health Monitoring [3.210027230758067]
  Self-attention achieved the lowest prediction error for subscale-level models.<n>We identified distinct homogeneity-hegeneity profiles across functional domains.
  arXiv  Detail & Related papers  (2025-07-13T02:56:40Z)
• NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification [42.418429168532406]
  We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification.<n>For efficient posterior inference, we introduce a mean-field variational algorithm with coordinate-ascent updates that scale linearly in model size.<n>In experiments, NeuralSurv delivers superior calibration compared to state-of-the-art deep survival models.
  arXiv  Detail & Related papers  (2025-05-16T09:53:21Z)
• Spectral structure learning for clinical time series [0.895367781592174]
  We develop and evaluate a structure learning algorithm for clinical time series.<n>Clinical time series are multivariate time series observed in multiple patients and irregularly sampled.<n>We propose a model of time series dependencies, that flexibly adapt to different time series regularity.
  arXiv  Detail & Related papers  (2025-02-17T11:13:39Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.