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Explainable Boosting Machine in Sepsis Prediction Using Platelet Metabolomics: An Interpretable Machine Learning

Emek Guldogan¹, Burak Yagin¹, Yavuz Korkmaz²

¹Department of Biostatistics, and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Türkiye.

Diagnostics (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces an interpretable machine learning model using platelet metabolomics for sepsis prediction, achieving high accuracy and providing clear explanations for clinical decision-making.

Keywords:

biomarkers clinical decision support explainable boosting machine machine learning metabolomics platelet metabolism sepsis

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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Biochemistry
Computational Biology
Clinical Medicine

Background:

Sepsis is a major cause of mortality in critical care settings, where early diagnosis is crucial.
Current sepsis prediction models lack transparency, hindering clinical trust and adoption.
Platelet metabolomics offers a potential but underexplored avenue for sepsis biomarker discovery.

Purpose of the Study:

To develop and validate an interpretable machine learning model for sepsis prediction using platelet metabolomics data.
To provide clinically meaningful explanations for sepsis prediction based on metabolic disturbances.
To enhance clinician trust and inform therapeutic strategies through transparent AI.

Main Methods:

Analysis of platelet metabolomics data from 25 sepsis patients and 14 controls using quantitative 1H-NMR spectroscopy.
Evaluation of five machine learning algorithms, including Explainable Boosting Machine (EBM), with biologically motivated metabolite ratios as features.
Utilized a nested leave-one-out cross-validation framework with data preprocessing and hyperparameter optimization within each fold.

Main Results:

The Explainable Boosting Machine (EBM) model achieved the highest performance, with an ROC-AUC of 0.864 and PR-AUC of 0.902.
Key predictors identified include Carnitine, myo-Inositol, ADP, and O-Phosphoethanolamine, along with interaction terms.
Local explanations highlighted the role of ADP-Creatine interaction, Glutamine, and myo-Inositol in a sepsis case.

Conclusions:

The EBM model demonstrates superior discriminative performance and calibration for sepsis prediction.
The model provides transparent mechanistic insights via global and local explanations, enhancing clinical interpretability.
This interpretable framework serves as a proof-of-concept, requiring external validation in larger cohorts for clinical application.