Temporal Sepsis Modeling: a Fully Interpretable Relational Way

• URL: http://arxiv.org/abs/2601.21747v1
• Date: Thu, 29 Jan 2026 14:02:26 GMT
• Title: Temporal Sepsis Modeling: a Fully Interpretable Relational Way
• Authors: Vincent Lemaire, Nédra Meloulli, Pierre Jaquet,
• Abstract summary: Deep learning models often lack interpretability and ignore latent patient sub-phenotypes.<n>We propose a machine learning framework by opening up a new avenue for addressing this issue: a relational approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable responses to treatment. While deep learning models perform well in the early prediction of sepsis, they often lack interpretability and ignore latent patient sub-phenotypes. In this work, we propose a machine learning framework by opening up a new avenue for addressing this issue: a relational approach. Temporal data from electronic medical records (EMRs) are viewed as multivariate patient logs and represented in a relational data schema. Then, a propositionalisation technique (based on classic aggregation/selection functions from the field of relational data) is applied to construct interpretable features to "flatten" the data. Finally, the flattened data is classified using a selective naive Bayesian classifier. Experimental validation demonstrates the relevance of the suggested approach as well as its extreme interpretability. The interpretation is fourfold: univariate, global, local, and counterfactual.

Related papers

• LUME-DBN: Full Bayesian Learning of DBNs from Incomplete data in Intensive Care [0.41292255339309664]
  We propose a novel Gibbs sampling-based method for learning Dynamic Bayesian networks from incomplete data.<n>We evaluate our method on both simulated datasets and real-world intensive care data from critically ill patients.
  arXiv  Detail & Related papers  (2025-11-06T13:13:39Z)
• Sequential Inference of Hospitalization Electronic Health Records Using Probabilistic Models [3.2988476179015005]
  In this work we design a probabilistic unsupervised model for multiple arbitrary-length sequences contained in hospitalization Electronic Health Record (EHR) data. The model uses a latent variable structure and captures complex relationships between medications, diagnoses, laboratory tests, neurological assessments, and medications. Inference algorithms are derived that use partial data to infer properties of the complete sequences, including their length and presence of specific values.
  arXiv  Detail & Related papers  (2024-03-27T21:06:26Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression [82.85825388788567]
  We develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data. We show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines.
  arXiv  Detail & Related papers  (2023-02-24T13:30:35Z)
• Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions [0.6875312133832078]
  We provide a comprehensive review of low-rank approximation-based approaches for computational phenotyping. Recent developments have adapted low-rank data approximation methods by incorporating different constraints and regularizations that facilitate interpretability further.
  arXiv  Detail & Related papers  (2022-09-01T09:47:27Z)
• MoReL: Multi-omics Relational Learning [26.484803417186384]
  We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across heterogeneous views. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, but also increases the model flexibility with the distribution-based regularization.
  arXiv  Detail & Related papers  (2022-03-15T02:50:07Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)

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.