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Updated: Mar 20, 2026
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis
Paul A Hilders1,2, Lada Lijović3,4, Martijn Otten3,5
1Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam Institute for Immunology and Infectious Diseases, Amsterdam UMC, Vrije Universiteit, University of Amsterdam, Amsterdam, The Netherlands. p.a.hilders@amsterdamumc.nl.
This study developed a novel machine learning approach to identify six distinct sepsis sub-phenotypes. These clinically relevant sepsis subtypes can inform personalized treatment strategies and improve patient outcomes.
Area of Science:
- * Computational biology and bioinformatics
- * Clinical informatics and data science
- * Machine learning in healthcare
Background:
- * Sepsis presents a significant global health challenge due to its complex and variable nature, impacting diagnosis, treatment, and prognosis.
- * Identifying distinct sepsis sub-phenotypes is crucial for tailoring interventions and improving patient outcomes.
- * Previous phenotyping efforts have shown limited clinical utility and inconsistent results.
Purpose of the Study:
- * To introduce a novel, guided machine-learning framework for identifying clinically relevant sepsis sub-phenotypes.
- * To capture temporal disease trajectories using deep representation learning and prediction-guided clustering.
- * To enhance the clinical utility of sepsis sub-phenotyping for personalized medicine.
Main Methods:
- * A recurrent neural network encoder was trained to generate patient representations guided by prediction objectives (mortality, length of stay, ventilation, renal replacement therapy).
- * K-means clustering was applied to the learned representations to identify distinct sub-phenotypes.
- * Sub-phenotypes were validated across multiple datasets (AmsterdamUMCdb, MIMIC-IV) and interpreted using Integrated Gradients. Clinical utility was assessed via reinforcement learning for treatment strategies.
Main Results:
- * Six clinically distinct sepsis sub-phenotypes were identified, each with unique risk profiles and clinical presentations.
- * The learned patient representations demonstrated strong generalizability across independent datasets.
- * Reinforcement learning analysis indicated that optimal treatment strategies varied significantly across the identified sub-phenotypes.
Conclusions:
- * A flexible and effective framework for identifying robust, clinically meaningful sepsis sub-phenotypes was developed.
- * The identified sub-phenotypes are clinically interpretable and support phenotype-informed decision-making.
- * This trajectory-aware phenotyping approach advances personalized and precision medicine strategies for sepsis management.
